Algorithms for disease diagnostics

ABSTRACT

The present invention relates to compositions and methods for molecular profiling and diagnostics for genetic disorders and cancer, including but not limited to gene expression product markers associated with cancer or genetic disorders. In particular, the present invention provides algorithms and methods of classifying cancer, for example, thyroid cancer, methods of determining molecular profiles, and methods of analyzing results to provide a diagnosis.

CROSS REFERENCE

This application is a continuation of U.S. patent application Ser. No.15/274,492 filed Sep. 23, 2016, which is a continuation of U.S. patentapplication Ser. No. 12/964,666 filed Dec. 9, 2010, which issued as U.S.Pat. No. 9,495,515 on Nov. 15, 2016, which claims the benefit of U.S.Provisional Application No. 61/285,165, entitled “Algorithms for DiseaseDiagnostics” filed Dec. 9, 2009, each of which is incorporated herein byreference in its entirety.

BACKGROUND OF THE INVENTION

A genetic disorder is an illness caused by abnormalities in genes orchromosomes. Some diseases, such as cancer, are due in part to geneticdisorders. Cancer is the second leading cause of death in the UnitedStates and one of the leading causes of mortality worldwide. Nearly 25million people are currently living with cancer, with 11 million newcases diagnosed each year. Furthermore, as the general populationcontinues to age, cancer will become a bigger and bigger problem. TheWorld Health Organization projects that by the year 2020, global cancerrates will increase by 50%.

Successful treatment of genetic diseases such as cancer starts withearly and accurate diagnosis. Current methods of diagnosis includecytological examination of tissue samples taken by biopsy or imaging oftissues and organs for evidence of aberrant cellular proliferation.While these techniques have proven to be both useful and inexpensive,they suffer from a number of drawbacks. First, cytological analysis andimaging techniques for cancer diagnosis often require a subjectiveassessment to determine the likelihood of malignancy. Second, theincreased use of these techniques has lead to a sharp increase in thenumber of indeterminate results in which no definitive diagnosis can bemade. Third, these routine diagnostic methods lack a rigorous method fordetermining the probability of an accurate diagnosis. Fourth, thesetechniques may be incapable of detecting a malignant growth at veryearly stages. Fifth, these techniques do not provide informationregarding the basis of the aberrant cellular proliferation.

Many of the newer generation of treatments for cancer, while exhibitinggreatly reduced side effects, are specifically targeted to a certainmetabolic or signaling pathway, and will only be effective againstcancers that are reliant on that pathway. Further, the cost of anytreatments can be prohibitive for an individual, insurance provider, orgovernment entity. This cost could be at least partially offset byimproved methods that accurately diagnose cancers and the pathways theyrely on at early stages. These improved methods would be useful both forpreventing unnecessary therapeutic interventions as well as directingtreatment.

In the case of thyroid cancer it is estimated that out of theapproximately 120,000 thyroid removal surgeries performed each year dueto suspected malignancy in the United States, only about 33,000 arenecessary. Thus, approximately 90,000 unnecessary surgeries areperformed at a cost of $7,000 each. In addition, there are continuedtreatment costs and complications due to the need for lifelong drugtherapy to replace the lost thyroid function. Accordingly, there is anunmet need for newer testing modalities and business practices thatimprove upon current methods of genetic disease diagnosis.

SUMMARY OF THE INVENTION

In one embodiment, the invention is a method of diagnosing a geneticdisorder or cancer comprising the steps of: (a) obtaining a biologicalsample comprising gene expression products; (b) detecting the geneexpression products of the biological sample; (c) comparing to an amountin a control sample, an amount of one or more gene expression productsin the biological sample to determine the differential gene expressionproduct level between the biological sample and the control sample; (d)classifying the biological sample by inputting the one or moredifferential gene expression product levels to a trained algorithm;wherein technical factor variables are removed from data based ondifferential gene expression product level and normalized prior to andduring classification; and (e) identifying the biological sample aspositive for a genetic disorder or cancer if the trained algorithmclassifies the sample as positive for the genetic disorder or cancer ata specified confidence level.

In another embodiment, the invention is an algorithm for diagnosing agenetic disorder or cancer comprising: (a) determining the level of geneexpression products in a biological sample; (b) deriving the compositionof cells in the biological sample based on the expression levels ofcell-type specific markers in the sample; (c) removing technicalvariables prior to and during classification of the biological sample;(d) correcting or normalizing the gene product levels determined in step(a) based on the composition of cells determined in step (b); and (e)classifying the biological sample as positive for a genetic disorder orcancer.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specificationare herein incorporated by reference to the same extent as if eachindividual publication or patent application was specifically andindividually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1 shows an example of technical factor effects using variancedecomposition analysis.

FIG. 2 shows that P-values improve after technical factor removal. Inthis example the technical factor removed was sample collection fluid.Two chemically distinct fluids were used for sample preservation at thetime of collection in the clinic. This technical factor obscured thebiological signal present during standard analysis. The 45-degree linein the graph indicates that the p-values calculated from the raw data(log 10 scale) were lower than the p-values calculated after thetechnical factor removal method was used on the same dataset. P-valuesbecome more significant with technical factor removal. Here technicalfactor is collection fluid. A large number of samples came from anothercollection fluid and that effect obscures the biological signal presentin the markers.

FIG. 3 depicts gene titration curves showing the algorithms of thepresent invention (Combo and Classification) improve classificationerror rates. Classification error rates using the “Tissue” gene list andclassification model to predict on the “FNA” cohort (Panel A).Classification error rates using the “FNA” and “Tissue” gene lists andthe FNA classification model to predict on the “FNA” cohort (Panel A).The left panel shows using tissue classifier and top tissue genes andpredict on FNA samples (N=49). The right panel shows using FNA(Classification) classifier and top FNA/tissue genes and predict on FNAsamples (N=49).

FIG. 4 depicts an honest assessment of classification performance byre-sampling the population to generate a second, subtype specific ROCcurve (N=167 FNA samples). The methods currently used in the art (blacktrace) underestimate classification errors compared to the methods ofthe present invention (red trace, panel A). This is more evident whenthe “indeterminate” pathology subtype is probed independently of allothers, and re-sampled to generate a second ROC curve of the data (panelB). The present invention improves the accuracy of classificationperformance calculation methods.

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure provides novel methods for diagnosing geneticdisorder or abnormal cellular proliferation from a biological testsample, and related kits and compositions. The present invention alsoprovides methods and compositions for differential diagnosis of types ofgenetic disorder or aberrant cellular proliferation such as carcinomas,adenomas, benign tumors, malignant tumors, and normal tissues. Thepresent invention further provides algorithms for characterizing andclassifying gene expression product markers and novel groups of geneexpression product markers useful for the diagnosis, characterization,and treatment of cellular proliferation or genetic disorder.Additionally the present invention provides business methods forproviding enhanced diagnosis, differential diagnosis, monitoring, andtreatment of cellular proliferation or genetic disorder. In oneembodiment, the algorithms of the present invention can be used fordiagnosing and monitoring thyroid cancer.

In the present invention, CEL files refer to raw data from an Affymetrixexon array. Electropherogram is used for visualization of the gel imagegenerated by the Bioanalyzer. An exon typically refers to a nucleic acidsequence that is represented in the mature form of an RNA molecule.Exons are protein-coding transcripts that are spliced before beingtranslated. An intron typically refers to a DNA region within a genethat is not translated into protein. Flash frozen paraffin embedded(FFPE)-RNA is known to be fragmented and degraded in such tissue.microRNAs (miRNA) typically refers to single-stranded RNA molecules of21-23 nucleotides in length, which regulate gene expression. NegativePredictive Value (NPV) is the proportion of patients with negative testresults who are correctly diagnosed. Positive predictive value (PPV), orprecision rate, or post-test probability of disease, is the proportionof patients with positive test results who are correctly diagnosed. Itis one of the most important measures of a diagnostic method as itreflects the probability that a positive test reflects the underlyingcondition being tested for. Receiver Operator Characteristic Curve (ROCcurve) plots sensitivity vs. (1—specificity) for a binary classifiersystem as its discrimination threshold is varied. Transcriptometypically refers to all mRNA transcripts with a particular cell ortissue. UHR refers to universal human RNA, which can be used as acontrol material.

I. Genetic Disorder and Aberrant Cell Proliferation

In one aspect, the algorithms and the methods disclosed herein can beused for diagnosis and monitoring of a genetic disorder. A geneticdisorder is an illness caused by abnormalities in genes or chromosomes.While some diseases, such as cancer, are due in part to geneticdisorders, they can also be caused by environmental factors. In someembodiments, the algorithms and the methods disclosed herein are usedfor diagnosis and monitoring of a cancer such as thyroid cancer.

Genetic disorders can be typically grouped into two categories: singlegene disorders and multifactorial and polygenic (complex) disorders. Asingle gene disorder is the result of a single mutated gene. There areestimated to be over 4000 human diseases caused by single gene defects.Single gene disorders can be passed on to subsequent generations inseveral ways. There are several types of inheriting a single genedisorder including but not limited to autosomal dominant, autosomalrecessive, X-linked dominant, X-linked recessive, Y-linked andmitochondrial inheritance. Only one mutated copy of the gene will benecessary for a person to be affected by an autosomal dominant disorder.Examples of autosomal dominant type of disorder include but are notlimited to Huntington's disease, Neurofibromatosis 1, Marfan Syndrome,Hereditary nonpolyposis colorectal cancer, and Hereditary multipleexostoses. In autosomal recessive disorder, two copies of the gene mustbe mutated for a person to be affected by an autosomal recessivedisorder. Examples of this type of disorder include but are not limitedto cystic fibrosis, sickle-cell disease (also partial sickle-celldisease), Tay-Sachs disease, Niemann-Pick disease, spinal muscularatrophy, and dry earwax. X-linked dominant disorders are caused bymutations in genes on the X chromosome. Only a few disorders have thisinheritance pattern, with a prime example being X-linkedhypophosphatemic rickets. Males and females are both affected in thesedisorders, with males typically being more severely affected thanfemales. Some X-linked dominant conditions such as Rett syndrome,Incontinentia Pigmenti type 2 and Aicardi Syndrome are usually fatal inmales either in utero or shortly after birth, and are thereforepredominantly seen in females. X-linked recessive disorders are alsocaused by mutations in genes on the X chromosome. Examples of this typeof disorder include but are not limited to Hemophilia A, Duchennemuscular dystrophy, red-green color blindness, muscular dystrophy andAndrogenetic alopecia. Y-linked disorders are caused by mutations on theY chromosome. Examples include but are not limited to Male Infertilityand hypertrichosis pinnae. Mitochondrial inheritance, also known asmaternal inheritance, applies to genes in mitochondrial DNA. An exampleof this type of disorder is Leber's Hereditary Optic Neuropathy.

Genetic disorders may also be complex, multifactorial or polygenic, thismeans that they are likely associated with the effects of multiple genesin combination with lifestyle and environmental factors. Althoughcomplex disorders often cluster in families, they do not have aclear-cut pattern of inheritance. This makes it difficult to determine aperson's risk of inheriting or passing on these disorders. Complexdisorders are also difficult to study and treat because the specificfactors that cause most of these disorders have not yet been identified.Multifactoral or polygenic disorders that can be diagnosed,characterized and/or monitored using the algorithms and methods of thepresent invention include but are not limited to heart disease,diabetes, asthma, autism, autoimmune diseases such as multiplesclerosis, cancers, ciliopathies, cleft palate, hypertension,inflammatory bowel disease, mental retardation and obesity.

Other genetic disorders that can be diagnosed, characterized and/ormonitored using the algorithms and methods of the present inventioninclude but are not limited to 1p36 deletion syndrome, 21-hydroxylasedeficiency, 22q11.2 deletion syndrome, 47, XYY syndrome, 48, XXXX, 49,XXXXX, aceruloplasminemia, achondrogenesis, type II, achondroplasia,acute intermittent porphyria, adenylosuccinate lyase deficiency,Adrenoleukodystrophy, ALA deficiency porphyria, ALA dehydratasedeficiency, Alexander disease, alkaptonuria, alpha-1 antitrypsindeficiency, Alstrom syndrome, Alzheimer's disease (type 1, 2, 3, and 4),Amelogenesis Imperfecta, amyotrophic lateral sclerosis, Amyotrophiclateral sclerosis type 2, Amyotrophic lateral sclerosis type 4,amyotrophic lateral sclerosis type 4, androgen insensitivity syndrome,Anemia, Angelman syndrome, Apert syndrome, ataxia-telangiectasia,Beare-Stevenson cutis gyrata syndrome, Benjamin syndrome, betathalassemia, biotinidase deficiency, Birt-Hogg-Dubé syndrome, bladdercancer, Bloom syndrome, Bone diseases, breast cancer, CADASIL,Camptomelic dysplasia, Canavan disease, Cancer, Celiac Disease, CGDChronic Granulomatous Disorder, Charcot-Marie-Tooth disease,Charcot-Marie-Tooth disease Type 1, Charcot-Marie-Tooth disease Type 4,Charcot-Marie-Tooth disease, type 2, Charcot-Marie-Tooth disease, type4, Cockayne syndrome, Coffin-Lowry syndrome, collagenopathy, types IIand XI, Colorectal Cancer, Congenital absence of the vas deferens,congenital bilateral absence of vas deferens, congenital diabetes,congenital erythropoietic porphyria, Congenital heart disease,congenital hypothyroidism, Connective tissue disease, Cowden syndrome,Cri du chat, Crohn's disease, fibrostenosing, Crouzon syndrome,Crouzonodermoskeletal syndrome, cystic fibrosis, De Grouchy Syndrome,Degenerative nerve diseases, Dent's disease, developmental disabilities,DiGeorge syndrome, Distal spinal muscular atrophy type V, Down syndrome,Dwarfism, Ehlers-Danlos syndrome, Ehlers-Danlos syndrome arthrochalasiatype, Ehlers-Danlos syndrome classical type, Ehlers-Danlos syndromedermatosparaxis type, Ehlers-Danlos syndrome kyphoscoliosis type,vascular type, erythropoietic protoporphyria, Fabry's disease, Facialinjuries and disorders, factor V Leiden thrombophilia, familialadenomatous polyposis, familial dysautonomia, fanconi anemia, FGsyndrome, fragile X syndrome, Friedreich ataxia, Friedreich's ataxia,G6PD deficiency, galactosemia, Gaucher's disease (type 1, 2, and 3),Genetic brain disorders, Glycine encephalopathy, Haemochromatosis type2, Haemochromatosis type 4, Harlequin Ichthyosis, Head and brainmalformations, Hearing disorders and deafness, Hearing problems inchildren, hemochromatosis (neonatal, type 2 and type 3), hemophilia,hepatoerythropoietic porphyria, hereditary coproporphyria, HereditaryMultiple Exostoses, hereditary neuropathy with liability to pressurepalsies, hereditary nonpolyposis colorectal cancer, homocystinuria,Huntington's disease, Hutchinson Gilford Progeria Syndrome,hyperoxaluria, primary, hyperphenylalaninemia, hypochondrogenesis,hypochondroplasia, idic15, incontinentia pigmenti, Infantile Gaucherdisease, infantile-onset ascending hereditary spastic paralysis,Infertility, Jackson-Weiss syndrome, Joubert syndrome, Juvenile PrimaryLateral Sclerosis, Kennedy disease, Klinefelter syndrome, Kniestdysplasia, Krabbe disease, Learning disability, Lesch-Nyhan syndrome,Leukodystrophies, Li-Fraumeni syndrome, lipoprotein lipase deficiency,familial, Male genital disorders, Marfan syndrome, McCune-Albrightsyndrome, McLeod syndrome, Mediterranean fever, familial, MEDNIK, Menkesdisease, Menkes syndrome, Metabolic disorders, methemoglobinemiabeta-globin type, Methemoglobinemia congenital methaemoglobinaemia,methylmalonic acidemia, Micro syndrome, Microcephaly, Movementdisorders, Mowat-Wilson syndrome, Mucopolysaccharidosis (MPS I), Muenkesyndrome, Muscular dystrophy, Muscular dystrophy, Duchenne and Beckertype, muscular dystrophy, Duchenne and Becker types, myotonic dystrophy,Myotonic dystrophy type 1 and type 2, Neonatal hemochromatosis,neurofibromatosis, neurofibromatosis 1, neurofibromatosis 2,Neurofibromatosis type I, neurofibromatosis type II, Neurologicdiseases, Neuromuscular disorders, Niemann-Pick disease, Nonketotichyperglycinemia, nonsyndromic deafness, Nonsyndromic deafness autosomalrecessive, Noonan syndrome, osteogenesis imperfecta (type I and typeIII), otospondylomegaepiphyseal dysplasia, pantothenatekinase-associated neurodegeneration, Patau Syndrome (Trisomy 13),Pendred syndrome, Peutz-Jeghers syndrome, Pfeiffer syndrome,phenylketonuria, porphyria, porphyria cutanea tarda, Prader-Willisyndrome, primary pulmonary hypertension, prion disease, Progeria,propionic acidemia, protein C deficiency, protein S deficiency,pseudo-Gaucher disease, pseudoxanthoma elasticum, Retinal disorders,retinoblastoma, retinoblastoma FA—Friedreich ataxia, Rett syndrome,Rubinstein-Taybi syndrome, SADDAN, Sandhoff disease, sensory andautonomic neuropathy type III, sickle cell anemia, skeletal muscleregeneration, Skin pigmentation disorders, Smith Lemli Opitz Syndrome,Speech and communication disorders, spinal muscular atrophy,spinal-bulbar muscular atrophy, spinocerebellar ataxia,spondyloepimetaphyseal dysplasia, Strudwick type, spondyloepiphysealdysplasia congenita, Stickler syndrome, Stickler syndrome COL2A1,Tay-Sachs disease, tetrahydrobiopterin deficiency, thanatophoricdysplasia, thiamine-responsive megaloblastic anemia with diabetesmellitus and sensorineural deafness, Thyroid disease, Tourette'sSyndrome, Treacher Collins syndrome, triple X syndrome, tuberoussclerosis, Turner syndrome, Usher syndrome, variegate porphyria, vonHippel-Lindau disease, Waardenburg syndrome, Weissenbacher-Zweymüllersyndrome, Wilson disease, Wolf-Hirschhorn syndrome, XerodermaPigmentosum, X-linked severe combined immunodeficiency, X-linkedsideroblastic anemia, and X-linked spinal-bulbar muscle atrophy.

Cancer is a leading cause of death in the United States. Early andaccurate diagnosis of cancer is critical for effective management ofthis disease. It is therefore important to develop testing modalitiesand business practices to enable cancer diagnosis more accurately, andearlier. Gene expression product profiling, also referred to asmolecular profiling, provides a powerful method for early and accuratediagnosis of tumors or other types of cancers from a biological sample.

Typically, screening for the presence of a tumor or other type ofcancer, involves analyzing a biological sample taken by various methodssuch as, for example, a biopsy. The biological sample is then preparedand examined by one skilled in the art. The methods of preparation caninclude but are not limited to various cytological stains, andimmuno-histochemical methods. Unfortunately, traditional methods ofcancer diagnosis suffer from a number of deficiencies. Thesedeficiencies include: 1) the diagnosis may require a subjectiveassessment and thus be prone to inaccuracy and lack of reproducibility,2) the methods may fail to determine the underlying genetic, metabolicor signaling pathways responsible for the resulting pathogenesis, 3) themethods may not provide a quantitative assessment of the test results,and 4) the methods may be unable to provide an unambiguous diagnosis forcertain samples.

One hallmark of cancer is dysregulation of normal transcriptionalcontrol leading to aberrant expression of genes. Among the aberrantlyexpressed genes are genes involved in cellular transformation, forexample tumor suppressors and oncogenes. Tumor suppressor genes andoncogenes may be up-regulated or down-regulated in tumors when comparedto normal tissues. Known tumor suppressors and oncogenes include, butare not limited to brca1, brca2, bcr-abl, bcl-2, HER2, N-myc, C-myc,BRAF, RET, Ras, KIT, Jun, Fos, and p53. This abnormal gene expressionmay occur through a variety of different mechanisms. It is not necessaryin the present invention to understand the mechanism of aberrant geneexpression, or the mechanism by which carcinogenesis occurs.Nevertheless, finding a gene or set of genes whose expression is up ordown regulated in a sample as compared to a normal sample may beindicative of cancer. Furthermore, the particular aberrantly expressedgene or set of genes may be indicative of a particular type of cancer,or even a recommended treatment protocol. Additionally the methods ofthe present invention are not meant to be limited solely to canonicallydefined tumor suppressors or oncogenes. Rather, it is understood thatany gene or set of genes that is determined to have a statisticallysignificant correlation with respect to expression level or splicing toa benign, malignant, or normal diagnosis is encompassed by the presentinvention.

In one embodiment, the methods of the present invention seek to improveupon the accuracy of current methods of cancer diagnosis. Improvedaccuracy may result from the measurement of multiple gene expressionmarkers, the identification of gene expression products with highdiagnostic power or statistical significance, or the identification ofgroups of gene expression products with high diagnostic power orstatistical significance, or any combination thereof.

For example, increased expression of a number of receptor tyrosinekinases has been implicated in carcinogenesis. Measurement of the geneexpression product level of a particular receptor tyrosine kinase knownto be differentially expressed in cancer cells may provide incorrectdiagnostic results leading to a low accuracy rate. Measurement of aplurality of receptor tyrosine kinases may increase the accuracy levelby requiring a combination of alternatively expressed genes to occur. Insome cases, measurement of a plurality of genes might therefore increasethe accuracy of a diagnosis by reducing the likelihood that a sample mayexhibit an aberrant gene expression profile by random chance.

Similarly, some gene expression products within a group such as receptortyrosine kinases may be indicative of a disease or condition when theirexpression levels are higher or lower than normal. The measurement ofexpression levels of other gene products within that same group may,however, provide no diagnostic utility. Therefore, it would beadvantageous to measure the expression levels of sets of genes thataccurately indicate the presence or absence of cancer from within agiven group.

Additionally, increased expression of other oncogenes such as forexample Ras in a biological sample may also be indicative of thepresence of cancerous cells. In some cases, it may be advantageous todetermine the expression level of several different classes of oncogenessuch as for example receptor tyrosine kinases, cytoplasmic tyrosinekinases, GTPases, serine/threonine kinases, lipid kinases, mitogens,growth factors, and transcription factors. The determination ofexpression levels and/or exon usage of different classes or groups ofgenes involved in cancer progression may in some cases increase thediagnostic power of the present invention.

Groups of gene expression markers may include markers within a metabolicor signaling pathway, or genetically or functionally homologous markers.For example, one group of markers may include genes involved in theepithelial growth factor signaling pathway. Another group of markers mayinclude mitogen-activated protein kinases. The present invention alsoprovides methods and compositions for detecting (i.e. measuring) andclassifying gene expression markers from multiple and/or independentmetabolic or signaling pathways.

In one embodiment, gene expression product markers of the presentinvention may provide increased accuracy of genetic disorder or cancerdiagnosis through the use of multiple gene expression product markers inlow quantity and quality, and statistical analysis using the algorithmsof the present invention. In particular, the present invention provides,but is not limited to, methods of diagnosing, characterizing andclassifying gene expression profiles associated with thyroid cancers.The present invention also provides algorithms for characterizing andclassifying thyroid tissue samples, and kits and compositions useful forthe application of said methods. The disclosure further includes methodsfor running a molecular profiling business.

In one embodiment, the subject methods and algorithm are used todiagnose, characterize, and monitor thyroid cancer. Other types ofcancer that can be diagnosed, characterized and/or monitored using thealgorithms and methods of the present invention include but are notlimited to adrenal cortical cancer, anal cancer, aplastic anemia, bileduct cancer, bladder cancer, bone cancer, bone metastasis, centralnervous system (CNS) cancers, peripheral nervous system (PNS) cancers,breast cancer, Castleman's disease, cervical cancer, childhoodNon-Hodgkin's lymphoma, colon and rectum cancer, endometrial cancer,esophagus cancer, Ewing's family of tumors (e.g. Ewing's sarcoma), eyecancer, gallbladder cancer, gastrointestinal carcinoid tumors,gastrointestinal stromal tumors, gestational trophoblastic disease,hairy cell leukemia, Hodgkin's disease, Kaposi's sarcoma, kidney cancer,laryngeal and hypopharyngeal cancer, acute lymphocytic leukemia, acutemyeloid leukemia, children's leukemia, chronic lymphocytic leukemia,chronic myeloid leukemia, liver cancer, lung cancer, lung carcinoidtumors, Non-Hodgkin's lymphoma, male breast cancer, malignantmesothelioma, multiple myeloma, myelodysplastic syndrome,myeloproliferative disorders, nasal cavity and paranasal cancer,nasopharyngeal cancer, neuroblastoma, oral cavity and oropharyngealcancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer,pituitary tumor, prostate cancer, retinoblastoma, rhabdomyosarcoma,salivary gland cancer, sarcoma (adult soft tissue cancer), melanoma skincancer, non-melanoma skin cancer, stomach cancer, testicular cancer,thymus cancer, uterine cancer (e.g. uterine sarcoma), vaginal cancer,vulvar cancer, and Waldenstrom's macroglobulinemia.

II. Obtaining a Biological Sample

The diagnosis of a genetic disorder or cancer may begin with anexamination of a subject by a physician, nurse or other medicalprofessional. As used herein, the term subject refers to any animal(e.g. a mammal), including but not limited to humans, non-humanprimates, rodents, dogs, pigs, and the like. The examination may be partof a routine examination, or the examination may be due to a specificcomplaint including but not limited to one of the following: pain,illness, anticipation of illness, presence of a suspicious lump or mass,a disease, or a condition. The subject may or may not be aware of thedisease or condition. The medical professional may obtain a biologicalsample for testing. In some cases the medical professional may refer thesubject to a testing center or laboratory for submission of thebiological sample.

In some cases, the subject may be referred to a specialist such as anoncologist, surgeon, or endocrinologist for further diagnosis. Thespecialist may likewise obtain a biological sample for testing or referthe individual to a testing center or laboratory for submission of thebiological sample. In any case, the biological sample may be obtained bya physician, nurse, or other medical professional such as a medicaltechnician, endocrinologist, cytologist, phlebotomist, radiologist, or apulmonologist. The medical professional may indicate the appropriatetest or assay to perform on the sample, or the molecular profilingbusiness of the present disclosure may consult on which assays or testsare most appropriately indicated. The molecular profiling business maybill the individual or medical or insurance provider thereof forconsulting work, for sample acquisition and or storage, for materials,or for all products and services rendered.

In some embodiments of the present invention, a medical professionalneed not be involved in the initial diagnosis or sample acquisition. Anindividual may alternatively obtain a sample through the use of an overthe counter kit. Said kit may contain a means for obtaining said sampleas described herein, a means for storing said sample for inspection, andinstructions for proper use of the kit. In some cases, molecularprofiling services are included in the price for purchase of the kit. Inother cases, the molecular profiling services are billed separately.

A sample suitable for use by the molecular profiling business may be anymaterial containing tissues, cells, genes, gene fragments, geneexpression products, or gene expression product fragments of anindividual to be tested. Methods for determining sample suitabilityand/or adequacy are provided. A sample may include but is not limitedto, tissue, cells, or biological material from cells or derived fromcells of an individual. The sample may be a heterogeneous or homogeneouspopulation of cells or tissues. In any case, the biological sample maybe obtained using any method known to the art that can provide a samplesuitable for the analytical methods described herein.

The sample may be obtained by non-invasive methods including but notlimited to: scraping of the skin or cervix, swabbing of the cheek,saliva collection, urine collection, feces collection, collection ofmenses, tears, or semen. In other cases, the sample is obtained by aninvasive procedure including but not limited to: biopsy, alveolar orpulmonary lavage, needle aspiration, or phlebotomy. The method of biopsymay further include incisional biopsy, excisional biopsy, punch biopsy,shave biopsy, or skin biopsy. The method of needle aspiration mayfurther include fine needle aspiration, core needle biopsy, vacuumassisted biopsy, or large core biopsy. In some embodiments, multiplesamples may be obtained by the methods herein to ensure a sufficientamount of biological material. Methods of obtaining suitable samples ofthyroid are known in the art and are further described in the ATAGuidelines for thyroid nodule management (Cooper et al. Thyroid Vol. 16No. 2 2006), herein incorporated by reference in its entirety. Genericmethods for obtaining biological samples are also known in the art andfurther described in for example Ramzy, Ibrahim Clinical Cytopathologyand Aspiration Biopsy 2001 which is herein incorporated by reference inits entirety. In one embodiment, the sample is a fine needle aspirate ofa thyroid nodule or a suspected thyroid tumor. In some cases, the fineneedle aspirate sampling procedure may be guided by the use of anultrasound, X-ray, or other imaging device.

In some embodiments of the present invention, the molecular profilingbusiness may obtain the biological sample from a subject directly, froma medical professional, from a third party, or from a kit provided bythe molecular profiling business or a third party. In some cases, thebiological sample may be obtained by the molecular profiling businessafter the subject, a medical professional, or a third party acquires andsends the biological sample to the molecular profiling business. In somecases, the molecular profiling business may provide suitable containers,and excipients for storage and transport of the biological sample to themolecular profiling business.

III. Test for Adequacy

Subsequent to or during sample acquisition, the biological material maybe collected and assessed for adequacy, for example, to asses thesuitability of the sample for use in the methods and compositions of thepresent invention. The assessment may be performed by the individual whoobtains the sample, the molecular profiling business, the individualusing a kit, or a third party such as a cytological lab, pathologist,endocrinologist, or a researcher. The sample may be determined to beadequate or inadequate for further analysis due to many factorsincluding but not limited to: insufficient cells, insufficient geneticmaterial, insufficient protein, DNA, or RNA, inappropriate cells for theindicated test, or inappropriate material for the indicated test, age ofthe sample, manner in which the sample was obtained, or manner in whichthe sample was stored or transported. Adequacy may be determined using avariety of methods known in the art such as a cell staining procedure,measurement of the number of cells or amount of tissue, measurement oftotal protein, measurement of nucleic acid, visual examination,microscopic examination, or temperature or pH determination. In oneembodiment, sample adequacy will be determined from the results ofperforming a gene expression product level analysis experiment. Inanother embodiment sample adequacy will be determined by measuring thecontent of a marker of sample adequacy. Such markers include elementssuch as iodine, calcium, magnesium, phosphorous, carbon, nitrogen,sulfur, iron etc.; proteins such as but not limited to thyroglobulin;cellular mass; and cellular components such as protein, nucleic acid,lipid, or carbohydrate.

In some cases, iodine may be measured by a chemical method such asdescribed in U.S. Pat. No. 3,645,691 which is incorporated herein byreference in its entirety or other chemical methods known in the art formeasuring iodine content. Chemical methods for iodine measurementinclude but are not limited to methods based on the Sandell and Kolthoffreaction. Said reaction proceeds according to the following equation:2Ce⁴⁺+As³+→2Ce³⁺+As⁵+I.

Iodine has a catalytic effect upon the course of the reaction, i.e., themore iodine present in the preparation to be analyzed, the more rapidlythe reaction proceeds. The speed of reaction is proportional to theiodine concentration. In some cases, this analytical method may carriedout in the following manner:

A predetermined amount of a solution of arsenous oxide As₂O₃ inconcentrated sulfuric or nitric acid is added to the biological sampleand the temperature of the mixture is adjusted to reaction temperature,i.e., usually to a temperature between 20° C. and 60° C. A predeterminedamount of a cerium (IV) sulfate solution in sulfuric or nitric acid isadded thereto. Thereupon, the mixture is allowed to react at thepredetermined temperature for a definite period of time. Said reactiontime is selected in accordance with the order of magnitude of the amountof iodine to be determined and with the respective selected reactiontemperature. The reaction time is usually between about 1 minute andabout 40 minutes. Thereafter, the content of the test solution of cerium(IV) ions is determined photometrically. The lower the photometricallydetermined cerium (IV) ion concentration is, the higher is the speed ofreaction and, consequently, the amount of catalytic agent, i.e., ofiodine. In this manner the iodine of the sample can directly andquantitatively be determined.

In other cases, iodine content of a sample of thyroid tissue may bemeasured by detecting a specific isotope of iodine such as for example¹²³I, ¹²⁴I, ¹²⁵I and ¹³¹I. In still other cases, the marker may beanother radioisotope such as an isotope of carbon, nitrogen, sulfur,oxygen, iron, phosphorous, or hydrogen. The radioisotope in someinstances may be administered prior to sample collection. Methods ofradioisotope administration suitable for adequacy testing are well knownin the art and include injection into a vein or artery, or by ingestion.A suitable period of time between administration of the isotope andacquisition of thyroid nodule sample so as to effect absorption of aportion of the isotope into the thyroid tissue may include any period oftime between about a minute and a few days or about one week includingabout 1 minute, 2 minutes, 5 minutes, 10 minutes, 15 minutes, ½ an hour,an hour, 8 hours, 12 hours, 24 hours, 48 hours, 72 hours, or about one,one and a half, or two weeks, and may readily be determined by oneskilled in the art. Alternatively, samples may be measured for naturallevels of isotopes such as radioisotopes of iodine, calcium, magnesium,carbon, nitrogen, sulfur, oxygen, iron, phosphorous, or hydrogen.

(i) Cell and/or Tissue Content Adequacy Test

Methods for determining the amount of a tissue include but are notlimited to weighing the sample or measuring the volume of sample.Methods for determining the amount of cells include but are not limitedto counting cells which may in some cases be performed afterdis-aggregation with for example an enzyme such as trypsin orcollagenase or by physical means such as using a tissue homogenizer forexample. Alternative methods for determining the amount of cellsrecovered include but are not limited to quantification of dyes thatbind to cellular material, or measurement of the volume of cell pelletobtained following centrifugation. Methods for determining that anadequate number of a specific type of cell is present include PCR,Q-PCR, RT-PCR, immuno-histochemical analysis, cytological analysis,microscopic, and or visual analysis.

(ii) Nucleic Acid Content Adequacy Test

Samples may be analyzed by determining nucleic acid content afterextraction from the biological sample using a variety of methods knownto the art. In some cases, RNA or mRNA is extracted from other nucleicacids prior to nucleic acid content analysis. Nucleic acid content maybe extracted, purified, and measured by ultraviolet absorbance,including but not limited to absorbance at 260 nanometers using aspectrophotometer. In other cases nucleic acid content or adequacy maybe measured by fluorometer after contacting the sample with a stain. Instill other cases, nucleic acid content or adequacy may be measuredafter electrophoresis, or using an instrument such as an agilentbioanalyzer for example. It is understood that the methods of thepresent invention are not limited to a specific method for measuringnucleic acid content and or integrity.

In some embodiments, the RNA quantity or yield from a given sample ismeasured shortly after purification using a NanoDrop spectrophotometerin a range of nano- to micrograms. In some embodiments, RNA quality ismeasured using an Agilent 2100 Bioanalyzer instrument, and ischaracterized by a calculated RNA Integrity Number (RIN, 1-10). TheNanoDrop is a cuvette-free spectrophotometer. It uses 1 microliter tomeasure from 5 ng/μl to 3,000 ng/μl of sample. The key features ofNanoDrop include low volume of sample and no cuvette; large dynamicrange 5 ng/μl to 3,000 ng/μl; and it allows quantitation of DNA, RNA andproteins. NanoDrop™ 2000c allows for the analysis of 0.5 μl-2.0 μlsamples, without the need for cuvettes or capillaries.

RNA quality can be measured by a calculated RNA Integrity Number (RIN).The RNA integrity number (RIN) is an algorithm for assigning integrityvalues to RNA measurements. The integrity of RNA is a major concern forgene expression studies and traditionally has been evaluated using the28S to 18S rRNA ratio, a method that has been shown to be inconsistent.The RIN algorithm is applied to electrophoretic RNA measurements andbased on a combination of different features that contribute informationabout the RNA integrity to provide a more robust universal measure. Insome embodiments, RNA quality is measured using an Agilent 2100Bioanalyzer instrument. The protocols for measuring RNA quality areknown and available commercially, for example, at Agilent website.Briefly, in the first step, researchers deposit total RNA sample into anRNA Nano LabChip. In the second step, the LabChip is inserted into theAgilent bioanalyzer and let the analysis run, generating a digitalelectropherogram. In the third step, the new RIN algorithm then analyzesthe entire electrophoretic trace of the RNA sample, including thepresence or absence of degradation products, to determine sampleintegrity. Then, The algorithm assigns a 1 to 10 RIN score, where level10 RNA is completely intact. Because interpretation of theelectropherogram is automatic and not subject to individualinterpretation, universal and unbiased comparison of samples is enabledand repeatability of experiments is improved. The RIN algorithm wasdeveloped using neural networks and adaptive learning in conjunctionwith a large database of eukaryote total RNA samples, which wereobtained mainly from human, rat, and mouse tissues. Advantages of RINinclude obtain a numerical assessment of the integrity of RNA; directlycomparing RNA samples, e.g. before and after archival, compare integrityof same tissue across different labs; and ensuring repeatability ofexperiments, e.g. if RIN shows a given value and is suitable formicroarray experiments, then the RIN of the same value can always beused for similar experiments given that the sameorganism/tissue/extraction method is used (Schroeder A, et al. BMCMolecular Biology 2006, 7:3 (2006)).

In some embodiments, RNA quality is measured on a scale of RIN 1 to 10,10 being highest quality. In one aspect, the present invention providesa method of analyzing gene expression from a sample with an RNA RINvalue equal or less than 6.0. In some embodiments, a sample containingRNA with an RIN number of 1.0, 2.0, 3.0, 4.0, 5.0 or 6.0 is analyzed formicroarray gene expression using the subject methods and algorithms ofthe present invention. In some embodiments, the sample is a fine needleaspirate of thyroid tissue. The sample can be degraded with an RIN aslow as 2.0.

Determination of gene expression in a given sample is a complex,dynamic, and expensive process. RNA samples with RIN ≤5.0 are typicallynot used for multi-gene microarray analysis, and may instead be usedonly for single-gene RT-PCR and/or TaqMan assays. This dichotomy in theusefulness of RNA according to quality has thus far limited theusefulness of samples and hampered research efforts. The presentinvention provides methods via which low quality RNA can be used toobtain meaningful multi-gene expression results from samples containinglow concentrations of RNA, for example, thyroid FNA samples.

In addition, samples having a low and/or un-measurable RNA concentrationby NanoDrop normally deemed inadequate for multi-gene expressionprofiling can be measured and analyzed using the subject methods andalgorithms of the present invention. The most sensitive and “state ofthe art” apparatus used to measure nucleic acid yield in the laboratorytoday is the NanoDrop spectrophotometer. Like many quantitativeinstruments of its kind, the accuracy of a NanoDrop measurementdecreases significantly with very low RNA concentration. The minimumamount of RNA necessary for input into a microarray experiment alsolimits the usefulness of a given sample. In the present invention, asample containing a very low amount of nucleic acid can be estimatedusing a combination of the measurements from both the NanoDrop and theBioanalyzer instruments, thereby optimizing the sample for multi-geneexpression assays and analysis.

(iii) Protein Content Adequacy Test

In some cases, protein content in the biological sample may be measuredusing a variety of methods known to the art, including but not limitedto: ultraviolet absorbance at 280 nanometers, cell staining as describedherein, or protein staining with for example coomassie blue, orbichichonic acid. In some cases, protein is extracted from thebiological sample prior to measurement of the sample. In some cases,multiple tests for adequacy of the sample may be performed in parallel,or one at a time. In some cases, the sample may be divided into aliquotsfor the purpose of performing multiple diagnostic tests prior to,during, or after assessing adequacy. In some cases, the adequacy test isperformed on a small amount of the sample which may or may not besuitable for further diagnostic testing. In other cases, the entiresample is assessed for adequacy. In any case, the test for adequacy maybe billed to the subject, medical provider, insurance provider, orgovernment entity.

In some embodiments of the present invention, the sample may be testedfor adequacy soon or immediately after collection. In some cases, whenthe sample adequacy test does not indicate a sufficient amount sample orsample of sufficient quality, additional samples may be taken.

IV. Storing the Sample

It may be advantageous to store samples prior to, during, or after useof the samples by the molecular profiling business. For example, samplesmay be stored upon acquisition to facilitate transport, or to wait forthe results of other analyses. In another embodiment, samples may bestored while awaiting instructions from a physician or other medicalprofessional. In some cases, a portion of the sample may be stored whileanother portion of said sample is further manipulated. Suchmanipulations may include but are not limited to molecular profiling,cytological staining, gene or gene expression product extraction,fixation, and examination.

The acquired sample may be placed in a suitable medium, excipient,solution, or container for short term or long term storage. Said storagemay require keeping the sample in a refrigerated, or frozen environment.The sample may be quickly frozen prior to storage in a frozenenvironment. The frozen sample may be contacted with a suitablecryopreservation medium or compound including but not limited to:glycerol, ethylene glycol, sucrose, or glucose. A suitable medium,excipient, or solution may include but is not limited to: hanks saltsolution, saline, cellular growth medium, or water. The medium,excipient, or solution may or may not be sterile.

The medium, excipient, or solution may contain preservative agents tomaintain the sample in an adequate state for subsequent diagnostics ormanipulation, or to prevent coagulation. Said preservatives may includecitrate, ethylene diamine tetraacetic acid, sodium azide, or thimersol.The sample may be fixed prior to or during storage by any method knownto the art such as using glutaraldehyde, formaldehyde, or methanol. Thecontainer may be any container suitable for storage and or transport ofthe biological sample including but not limited to: a cup, a cup with alid, a tube, a sterile tube, a vacuum tube, a syringe, a bottle, amicroscope slide, or any other suitable container. The container may ormay not be sterile. In some cases, the sample may be stored in acommercial preparation suitable for storage of cells for subsequentcytological analysis such as but not limited to Cytyc ThinPrep,SurePath, or Monoprep.

V. Transportation of the Sample

The sample may be transported to the molecular profiling company of thepresent disclosure in order to perform the analyses described herein.The sample may be transported by the individual from whom the samplederives. Said transportation by the individual may include theindividual appearing at the molecular profiling business or a designatedsample receiving point and providing a sample. Said providing of thesample may involve any of the techniques of sample acquisition describedherein, or the sample may have already have been acquired and stored ina suitable container as described herein. In other cases the sample maybe transported to the molecular profiling business using a courierservice, the postal service, a shipping service, or any method capableof transporting the sample in a suitable manner. In some cases, thesample may be provided to the molecular profiling business by a thirdparty testing laboratory (e.g. a cytology lab). In other cases, thesample may be provided to the molecular profiling business by thesubject's primary care physician, endocrinologist or other medicalprofessional. The cost of transport may be billed to the individual,medical provider, or insurance provider. The molecular profilingbusiness may begin analysis of the sample immediately upon receipt, ormay store the sample in any manner described herein. The method ofstorage may or may not be the same as chosen prior to receipt of thesample by the molecular profiling business.

VI. Analysis of Sample

Upon receipt of the sample by the molecular profiling business, arepresentative or licensee thereof, a medical professional, researcher,or a third party laboratory or testing center (e.g. a cytologylaboratory) the sample may be assayed using a variety of routineanalyses known to the art such as cytological assays, and genomicanalysis. Such tests may be indicative of cancer, the type of cancer,any other disease or condition, the presence of disease markers, or theabsence of cancer, diseases, conditions, or disease markers. The testsmay take the form of cytological examination including microscopicexamination as described below. The tests may involve the use of one ormore cytological stains. The biological material may be manipulated orprepared for the test prior to administration of the test by anysuitable method known to the art for biological sample preparation. Thespecific assay performed may be determined by the molecular profilingcompany, the physician who ordered the test, or a third party such as aconsulting medical professional, cytology laboratory, the subject fromwhom the sample derives, or an insurance provider. The specific assaymay be chosen based on the likelihood of obtaining a definite diagnosis,the cost of the assay, the speed of the assay, or the suitability of theassay to the type of material provided.

The present disclosure provides methods and compositions for improvingupon the current state of the art for diagnosing genetic disorders orcancer. In one aspect, the present invention provides methods forperforming microarray gene expression analysis with low quantity andquality of polynucleotide, such as DNA or RNA. In some embodiments, thepresent disclosure describes methods of diagnosing, characterizingand/or monitoring a cancer by analyzing gene expression with lowquantity and quality of RNA. In one embodiment, the cancer is thyroidcancer. Thyroid RNA can be obtained from fine needle aspirates (FNA). Insome embodiments, gene expression profile is obtained from degradedsamples with an RNA RIN value of 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0,1.0 or less. In particular embodiments, gene expression profile isobtained from a sample with an RIN of equal or less than 6, i.e. 6.0,5.0, 4.0, 3.0, 2.0, 1.0 or less. Provided by the present invention aremethods by which low quality RNA can be used to obtain meaningful geneexpression results from samples containing low concentrations of nucleicacid, such as thyroid FNA samples.

Another estimate of sample usefulness is RNA yield, typically measuredin nanogram to microgram amounts for gene expression assays. The mostsensitive and “state of the art” apparatus used to measure nucleic acidyield in the laboratory today is the NanoDrop spectrophotometer. Likemany quantitative instruments of its kind, the accuracy of a NanoDropmeasurement decreases significantly with very low RNA concentration. Theminimum amount of RNA necessary for input into a microarray experimentalso limits the usefulness of a given sample. In some aspects, thepresent invention solves the low RNA concentration problem by estimatingsample input using a combination of the measurements from both theNanoDrop and the Bioanalyzer instruments. Since the quality of dataobtained from a gene expression study is dependent on RNA quantity,meaningful gene expression data can be generated from samples having alow or un-measurable RNA concentration as measured by NanoDrop.

The subject methods and algorithms enable: 1) gene expression analysisof samples containing low amount and/or low quality of nucleic acid; 2)a significant reduction of false positives and false negatives, 3) adetermination of the underlying genetic, metabolic, or signalingpathways responsible for the resulting pathology, 4) the ability toassign a statistical probability to the accuracy of the diagnosis ofgenetic disorders, 5) the ability to resolve ambiguous results, and 6)the ability to distinguish between sub-types of cancer.

VII. Cytological Analysis

Samples may be analyzed by cell staining combined with microscopicexamination of the cells in the biological sample. Cell staining, orcytological examination, may be performed by a number of methods andsuitable reagents known to the art including but not limited to: EAstains, hematoxylin stains, cytostain, papanicolaou stain, eosin, nisslstain, toluidine blue, silver stain, azocarmine stain, neutral red, orjanus green. In some cases the cells are fixed and/or permeabilized withfor example methanol, ethanol, glutaraldehyde or formaldehyde prior toor during the staining procedure. In some cases, the cells are notfixed. In some cases, more than one stain is used in combination. Inother cases no stain is used at all. In some cases measurement ofnucleic acid content is performed using a staining procedure, forexample with ethidium bromide, hematoxylin, nissl stain or any nucleicacid stain known to the art.

In some embodiments of the present invention, cells may be smeared ontoa slide by standard methods well known in the art for cytologicalexamination. In other cases, liquid based cytology (LBC) methods may beutilized. In some cases, LBC methods provide for an improved means ofcytology slide preparation, more homogenous samples, increasedsensitivity and specificity, and improved efficiency of handling ofsamples. In liquid based cytology methods, biological samples aretransferred from the subject to a container or vial containing a liquidcytology preparation solution such as for example Cytyc ThinPrep,SurePath, or Monoprep or any other liquid based cytology preparationsolution known in the art. Additionally, the sample may be rinsed fromthe collection device with liquid cytology preparation solution into thecontainer or vial to ensure substantially quantitative transfer of thesample. The solution containing the biological sample in liquid basedcytology preparation solution may then be stored and/or processed by amachine to produce a layer of cells on a glass slide. The sample mayfurther be stained and examined under the microscope in the same way asa conventional cytological preparation.

In some embodiments of the present invention, samples may be analyzed byimmuno-histochemical staining. Immuno-histochemical staining providesfor the analysis of the presence, location, and distribution of specificmolecules or antigens by use of antibodies in a biological sample (e.g.cells or tissues). Antigens may be small molecules, proteins, peptides,nucleic acids or any other molecule capable of being specificallyrecognized by an antibody. Samples may be analyzed byimmuno-histochemical methods with or without a prior fixing and/orpermeabilization step. In some cases, the antigen of interest may bedetected by contacting the sample with an antibody specific for theantigen and then non-specific binding may be removed by one or morewashes. The specifically bound antibodies may then be detected by anantibody detection reagent such as for example a labeled secondaryantibody, or a labeled avidin/streptavidin. In some cases, the antigenspecific antibody may be labeled directly instead. Suitable labels forimmuno-histochemistry include but are not limited to fluorophores suchas fluoroscein and rhodamine, enzymes such as alkaline phosphatase andhorse radish peroxidase, and radionuclides such as ³²P and ¹²⁵I. Geneproduct markers that may be detected by immuno-histochemical staininginclude but are not limited to Her2/Neu, Ras, Rho, EGFR, VEGFR, UbcH10,RET/PTC1, cytokeratin 20, calcitonin, GAL-3, thyroid peroxidase, andthyroglobulin.

(i) Tissue-Type Fingerprinting

In many cases, biological samples such as those provided by the methodsof the present invention of may contain several cell types or tissues,including but not limited to thyroid follicular cells, thyroid medullarycells, blood cells (RBCs, WBCs, platelets), smooth muscle cells, ducts,duct cells, basement membrane, lumen, lobules, fatty tissue, skin cells,epithelial cells, and infiltrating macrophages and lymphocytes. In thecase of thyroid samples, diagnostic classification of the biologicalsamples may involve for example primarily follicular cells (for cancersderived from the follicular cell such as papillary carcinoma, follicularcarcinoma, and anaplastic thyroid carcinoma) and medullary cells (formedullary cancer). Since medullary and anaplastic thyroid cancers arerarely present in thyroid samples classified as indeterminate, thediagnosis of indeterminate biological samples from thyroid biopsies insome cases concerns the distinction of follicular adenoma vs. follicularcarcinoma. The gene expression signal of a follicular cell for examplemay thus be diluted out and possibly confounded by other cell typespresent in the sample. Similarly diagnosis of biological samples fromother tissues or organs often involves diagnosing one or more cell typesamong the many that may be present in the sample.

In some embodiments, the methods of the present invention provide for anupfront method of determining the cellular make-up of a particularbiological sample so that the resulting gene expression signatures canbe calibrated against the dilution effect due to the presence of othercell and/or tissue types. In one aspect, this upfront method is analgorithm that uses a combination of known cell and/or tissue specificgenes as an upfront mini-classifier for each component of the sample.This algorithm utilizes this molecular fingerprint to pre-classify thesamples according to their composition and then apply acorrection/normalization factor. This data may in some cases then feedin to a final classification algorithm which would incorporate thatinformation to aid in the final diagnosis.

(ii) Genomic Analysis

In some embodiments, genomic sequence analysis, or genotyping, may beperformed on the sample. This genotyping may take the form of mutationalanalysis such as single nucleotide polymorphism (SNP) analysis,insertion deletion polymorphism (InDel) analysis, variable number oftandem repeat (VNTR) analysis, copy number variation (CNV) analysis orpartial or whole genome sequencing. Methods for performing genomicanalyses are known to the art and may include high throughput sequencingsuch as but not limited to those methods described in U.S. Pat. Nos.7,335,762; 7,323,305; 7,264,929; 7,244,559; 7,211,390; 7,361,488;7,300,788; and 7,280,922. Methods for performing genomic analyses mayalso include microarray methods as described hereinafter. In some cases,genomic analysis may be performed in combination with any of the othermethods herein. For example, a sample may be obtained, tested foradequacy, and divided into aliquots. One or more aliquots may then beused for cytological analysis of the present invention, one or more maybe used for gene expression profiling methods of the present invention,and one or more may be used for genomic analysis. It is furtherunderstood the present invention anticipates that one skilled in the artmay wish to perform other analyses on the biological sample that are notexplicitly provided herein.

(iii) Gene Expression Product Profiling

Gene expression profiling is the measurement of the activity (theexpression) of thousands of genes at once, to create a global picture ofcellular function. These profiles can, for example, distinguish betweencells that are actively dividing, or show how the cells react to aparticular treatment. Many experiments of this sort measure an entiregenome simultaneously, that is, every gene present in a particular cell.Microarray technology measures the relative activity of previouslyidentified target genes. Sequence based techniques, like serial analysisof gene expression (SAGE, SuperSAGE) are also used for gene expressionprofiling. SuperSAGE is especially accurate and can measure any activegene, not just a predefined set. In an mRNA or gene expression profilingmicroarray, the expression levels of thousands of genes aresimultaneously monitored to study the effects of certain treatments,diseases, and developmental stages on gene expression. For example,microarray-based gene expression profiling can be used to characterizegene signatures of a genetic disorder disclosed herein, or differentcancer types, subtypes of a cancer, and/or cancer stages.

Expression profiling experiments often involve measuring the relativeamount of mRNA expressed in two or more experimental conditions. This isbecause altered levels of a specific sequence of mRNA suggest a changedneed for the protein coded for by the mRNA, perhaps indicating ahomeostatic response or a pathological condition. For example, if breastcancer cells express higher levels of mRNA associated with a particulartransmembrane receptor than normal cells do, it might be that thisreceptor plays a role in breast cancer. One aspect of the presentinvention encompasses gene expression profiling as part of an importantdiagnostic test for genetic disorders and cancers, particularly, thyroidcancer.

In some embodiments, RNA samples with RIN ≤5.0 are typically not usedfor multi-gene microarray analysis, and may instead be used only forsingle-gene RT-PCR and/or TaqMan assays. Microarray, RT-PCR and TaqManassays are standard molecular techniques well known in the relevant art.TaqMan probe-based assays are widely used in real-time PCR includinggene expression assays, DNA quantification and SNP genotyping.

In one embodiment, gene expression products related to cancer that areknown to the art are profiled. Such gene expression products have beendescribed and include but are not limited to the gene expressionproducts detailed in U.S. Pat. Nos. 7,358,061; 7,319,011; 5,965,360;6,436,642; and US patent applications 2003/0186248, 2005/0042222,2003/0190602, 2005/0048533, 2005/0266443, 2006/0035244, 2006/083744,2006/0088851, 2006/0105360, 2006/0127907, 2007/0020657, 2007/0037186,2007/0065833, 2007/0161004, 2007/0238119, and 2008/0044824.

It is further anticipated that other gene expression products related tocancer may become known, and that the methods and compositions describedherein may include such newly discovered gene expression products.

In some embodiments of the present invention gene expression productsare analyzed alternatively or additionally for characteristics otherthan expression level. For example, gene products may be analyzed foralternative splicing. Alternative splicing, also referred to asalternative exon usage, is the RNA splicing variation mechanism whereinthe exons of a primary gene transcript, the pre-mRNA, are separated andreconnected (i.e. spliced) so as to produce alternative mRNA moleculesfrom the same gene. In some cases, these linear combinations thenundergo the process of translation where a specific and unique sequenceof amino acids is specified by each of the alternative mRNA moleculesfrom the same gene resulting in protein isoforms. Alternative splicingmay include incorporating different exons or different sets of exons,retaining certain introns, or using utilizing alternate splice donor andacceptor sites.

In some cases, markers or sets of markers may be identified that exhibitalternative splicing that is diagnostic for benign, malignant or normalsamples. Additionally, alternative splicing markers may further providea diagnosis for the specific type of thyroid cancer (e.g. papillary,follicular, medullary, or anaplastic). Alternative splicing markersdiagnostic for malignancy known to the art include those listed in U.S.Pat. No. 6,436,642.

(1) In Vitro Methods of Determining Gene Expression Product Levels

The general methods for determining gene expression product levels areknown to the art and may include but are not limited to one or more ofthe following: additional cytological assays, assays for specificproteins or enzyme activities, assays for specific expression productsincluding protein or RNA or specific RNA splice variants, in situhybridization, whole or partial genome expression analysis, microarrayhybridization assays, SAGE, enzyme linked immuno-absorbance assays,mass-spectrometry, immuno-histochemistry, or blotting. Gene expressionproduct levels may be normalized to an internal standard such as totalmRNA or the expression level of a particular gene including but notlimited to glyceraldehyde 3 phosphate dehydrogenase, or tublin.

In some embodiments of the present invention, gene expression productmarkers and alternative splicing markers may be determined by microarrayanalysis using, for example, Affymetrix arrays, cDNA microarrays,oligonucleotide microarrays, spotted microarrays, or other microarrayproducts from Biorad, Agilent, or Eppendorf. Microarrays provideparticular advantages because they may contain a large number of genesor alternative splice variants that may be assayed in a singleexperiment. In some cases, the microarray device may contain the entirehuman genome or transcriptome or a substantial fraction thereof allowinga comprehensive evaluation of gene expression patterns, genomicsequence, or alternative splicing. Markers may be found using standardmolecular biology and microarray analysis techniques as described inSambrook Molecular Cloning a Laboratory Manual 2001 and Baldi, P., andHatfield, W. G., DNA Microarrays and Gene Expression 2002.

Microarray analysis begins with extracting and purifying nucleic acidfrom a biological sample, (e.g. a biopsy or fine needle aspirate) usingmethods known to the art. For expression and alternative splicinganalysis it may be advantageous to extract and/or purify RNA from DNA.It may further be advantageous to extract and/or purify mRNA from otherforms of RNA such as tRNA and rRNA.

Purified nucleic acid may further be labeled with a fluorescent,radionuclide, or chemical label such as biotin or digoxin for example byreverse transcription, PCR, ligation, chemical reaction or othertechniques. The labeling can be direct or indirect which may furtherrequire a coupling stage. The coupling stage can occur beforehybridization, for example, using aminoallyl-UTP and NHS amino-reactivedyes (like cyanine dyes) or after, for example, using biotin and labeledstreptavidin. The modified nucleotides (e.g. at a 1 aaUTP:4 TTP ratio)are added enzymatically at a lower rate compared to normal nucleotides,typically resulting in 1 every 60 bases (measured with aspectrophotometer). The aaDNA may then be purified with, for example, acolumn or a diafiltration device. The aminoallyl group is an amine groupon a long linker attached to the nucleobase, which reacts with areactive label (e.g. a fluorescent dye).

The labeled samples may then be mixed with a hybridization solutionwhich may contain SDS, SSC, dextran sulfate, a blocking agent (such asCOT1 DNA, salmon sperm DNA, calf thymum DNA, PolyA or PolyT), Denhardt'ssolution, formamine, or a combination thereof. A hybridization probe isa fragment of DNA or RNA of variable length, which is used to detect inDNA or RNA samples the presence of nucleotide sequences (the DNA target)that are complementary to the sequence in the probe. The probe therebyhybridizes to single-stranded nucleic acid (DNA or RNA) whose basesequence allows probe-target base pairing due to complementarity betweenthe probe and target. The labeled probe is first denatured (by heatingor under alkaline conditions) into single DNA strands and thenhybridized to the target DNA.

To detect hybridization of the probe to its target sequence, the probeis tagged (or labeled) with a molecular marker; commonly used markersare ³²P or Digoxigenin, which is non-radioactive antibody-based marker.DNA sequences or RNA transcripts that have moderate to high sequencesimilarity to the probe are then detected by visualizing the hybridizedprobe via autoradiography or other imaging techniques. Detection ofsequences with moderate or high similarity depends on how stringent thehybridization conditions were applied—high stringency, such as highhybridization temperature and low salt in hybridization buffers, permitsonly hybridization between nucleic acid sequences that are highlysimilar, whereas low stringency, such as lower temperature and highsalt, allows hybridization when the sequences are less similar.Hybridization probes used in DNA microarrays refer to DNA covalentlyattached to an inert surface, such as coated glass slides or gene chips,and to which a mobile cDNA target is hybridized.

This mix may then be denatured by heat or chemical means and added to aport in a microarray. The holes may then be sealed and the microarrayhybridized, for example, in a hybridization oven, where the microarrayis mixed by rotation, or in a mixer. After an overnight hybridization,non specific binding may be washed off (e.g. with SDS and SSC). Themicroarray may then be dried and scanned in a special machine where alaser excites the dye and a detector measures its emission. The imagemay be overlaid with a template grid and the intensities of the features(several pixels make a feature) may be quantified

Various kits can be used for the amplification of nucleic acid and probegeneration of the subject methods. Examples of kit that can be used inthe present invention include but are not limited to Nugen WT-OvationFFPE kit, cDNA amplification kit with Nugen Exon Module and Frag/Labelmodule. The NuGEN WT-Ovation™ FFPE System V2 is a whole transcriptomeamplification system that enables conducting global gene expressionanalysis on the vast archives of small and degraded RNA derived fromFFPE samples. The system is comprised of reagents and a protocolrequired for amplification of as little as 50 ng of total FFPE RNA. Theprotocol can be used for qPCR, sample archiving, fragmentation, andlabeling. The amplified cDNA can be fragmented and labeled in less thantwo hours for GeneChip® 3′ expression array analysis using NuGEN'sFL-Ovation™ cDNA Biotin Module V2. For analysis using AffymetrixGeneChip® Exon and Gene ST arrays, the amplified cDNA can be used withthe WT-Ovation Exon Module, then fragmented and labeled using theFL-Ovation™ cDNA Biotin Module V2. For analysis on Agilent arrays, theamplified cDNA can be fragmented and labeled using NuGEN's FL-Ovation™cDNA Fluorescent Module.

In some embodiments, Ambion WT-expression kit can be used. AmbionWT-expression kit allows amplification of total RNA directly without aseparate ribosomal RNA (rRNA) depletion step. With the Ambion® WTExpression Kit, samples as small as 50 ng of total RNA can be analyzedon Affymetrix® GeneChip® Human, Mouse, and Rat Exon and Gene 1.0 STArrays. In addition to the lower input RNA requirement and highconcordance between the Affymetrix® method and TaqMan® real-time PCRdata, the Ambion® WT Expression Kit provides a significant increase insensitivity. For example, a greater number of probe sets detected abovebackground can be obtained at the exon level with the Ambion® WTExpression Kit as a result of an increased signal-to-noise ratio. AmbionWT-expression kit may be used in combination with additional Affymetrixlabeling kit.

In some embodiments, AmpTec Trinucleotide Nano mRNA Amplification kit(6299-A15) can be used in the subject methods. The ExpressArt®TRinucleotide mRNA amplification Nano kit is suitable for a wide range,from 1 ng to 700 ng of input total RNA. According to the amount of inputtotal RNA and the required yields of aRNA, it can be used for 1-round(input >300 ng total RNA) or 2-rounds (minimal input amount 1 ng totalRNA), with aRNA yields in the range of >10 μg. AmpTec's proprietaryTRinucleotide priming technology results in preferential amplificationof mRNAs (independent of the universal eukaryotic 3′-poly(A)-sequence),combined with selection against rRNAs. This kit can be used incombination with cDNA conversion kit and Affymetrix labeling kit.

The raw data may then be normalized, for example, by subtracting thebackground intensity and then dividing the intensities making either thetotal intensity of the features on each channel equal or the intensitiesof a reference gene and then the t-value for all the intensities may becalculated. More sophisticated methods include z-ratio, loess and lowessregression and RMA (robust multichip analysis) for Affymetrix chips.

(2) In Vivo Methods of Determining Gene Expression Product Levels

It is further anticipated that the methods and compositions of thepresent invention may be used to determine gene expression productlevels in an individual without first obtaining a sample. For example,gene expression product levels may be determined in vivo, that is in theindividual. Methods for determining gene expression product levels invivo are known to the art and include imaging techniques such as CAT,MRI; NMR; PET; and optical, fluorescence, or biophotonic imaging ofprotein or RNA levels using antibodies or molecular beacons. Suchmethods are described in US 2008/0044824, US 2008/0131892, hereinincorporated by reference. Additional methods for in vivo molecularprofiling are contemplated to be within the scope of the presentinvention.

(iv) Assay Results

The results of the routine cytological, genomic, and/or molecularprofiling assays may indicate a sample as negative (cancer, disease orcondition free), ambiguous or suspicious (suggestive of the presence ofa cancer, disease or condition), diagnostic (positive diagnosis for acancer, disease or condition), or non diagnostic (providing inadequateinformation concerning the presence or absence of cancer, disease, orcondition). The diagnostic results may be further classified asmalignant or benign. The diagnostic results may also provide a scoreindicating for example, the severity or grade of a cancer, or thelikelihood of an accurate diagnosis. In some cases, the diagnosticresults may be indicative of a particular type or stage of a cancer,disease, or condition. The diagnostic results may inform a particulartreatment or therapeutic intervention for the type or stage of thespecific cancer disease or condition diagnosed. In some embodiments, theresults of the assays performed may be entered into a database. Themolecular profiling company may bill the individual, insurance provider,medical provider, or government entity for one or more of the following:assays performed, consulting services, reporting of results, databaseaccess, or data analysis. In some cases all or some steps other thanmolecular profiling are performed by a cytological laboratory or amedical professional.

VII. Molecular Profiling with Algorithms

The herein described methods and algorithms can be used as a diagnostictool for many types of genetic disorders or suspected tumors includingfor example thyroid tumors or nodules. Samples that assay as negative,indeterminate, diagnostic, or non diagnostic may be subjected tosubsequent assays to obtain more information. In the present invention,these subsequent assays comprise the steps of molecular profiling ofgene expression product levels or gene expression product alternativesplicing. In some embodiments of the present invention, molecularprofiling means the determination of the number (i.e. amount) and/ortype of gene expression product molecules (i.e. nucleic acid or protein)in a biological sample. In some cases, the number and/or type mayfurther be compared to a control sample or a sample considered to benormal. Molecular profiling may be performed on the same sample, aportion of the same sample, or a new sample may be acquired using any ofthe methods described herein. The molecular profiling company mayrequest additional sample by directly contacting the individual orthrough an intermediary such as a physician, third party testing centeror laboratory, or a medical professional. In some cases, samples areassayed using methods and compositions of the molecular profilingbusiness in combination with some or all cytological staining or otherdiagnostic methods. In other cases, samples are directly assayed usingthe methods and compositions of the molecular profiling business withoutthe previous use of routine cytological staining or other diagnosticmethods. In some cases the results of molecular profiling alone or incombination with cytology may enable those skilled in the art todiagnose or suggest treatment for the subject. In some cases, molecularprofiling may be used alone or in combination with cytology to monitortumors or suspected tumors over time for malignant changes.

The goal of algorithm development is to extract biological informationfrom high-dimensional transcription data in order to accurately classifybenign vs. malignant biopsies. In some embodiments, disclosed herein isa molecular classifier algorithm, which combines the process of upfrontpre-processing of exon data, followed by exploratory analysis, technicalfactor removal (when necessary), feature (i.e. marker or gene)selection, classification, and finally, performance measurements. Otherembodiments also describe the process of cross-validation within andoutside the feature selection loop as well as an iterative geneselection method algorithm to combine three analytical methods of markerextraction (tissue, Bayesian, repeatability).

In some embodiments, the present invention comprises 3 distinct phases:first, improve sample collection and nucleic acid extraction incompromised FNA samples; second, collect high-quality genome-wideexpression data on an adequate number of samples; and finally, create,train and test an algorithm which could use high-dimensionality data andaccurately classify FNAs into a benign or malignant state. Exemplarymaterials and methods used for each of these three phases are disclosedherein.

In some embodiments, a series of experiments designed to determinefeasibility of a molecular thyroid test are performed. The test is aimedat classifying thyroid fine needle aspirates (FNAs) into benign andmalignant categories. In one example, a total of 11 major experimentsassaying over 690 specimens were designed and executed, all leading tomajor conclusions or decisions. In some embodiments, the presentinvention comprises two general categories: assay development andalgorithm development. For example, in some embodiments, the followingassay parameters can be used:

Selection of RNAProtect as FNA Collection Fluid

Selection of 25 ng as RNA input used for NuGEN amplification

Selection of NuGEN FFPE kit for amplification

Selection of microarray platform for analysis of degraded RNA samples

In several embodiments, algorithm training can be conducted on a sampleof surgical tissue, FNA's collected in TRIzol, and/or FNAs collected inRNAProtect. In testing of algorithm performance results, results wereobtained where approximately 90% non-benign percent agreement (akasensitivity) and 93% benign agreement (aka specificity) on a select setof samples that pass certain pre- and post-chip metrics.

Training of the algorithm can include feature (ie. Gene) selection. Eachround of training can result in a de novo set of markers, for example,5, 10, 25, 50, 100, 200, 300 or 500 markers. In one example, comparisonof marker lists across the three key discovery training sets (surgicaltissue, FNA's collected in TRIzol, and FNAs collected in RNAProtect)revealed a total of 338 non-redundant markers; of these 158 markers arein all three marker lists.

In addition to assay and algorithm development, the selection ofAffymetrix is based on three criteria: technical feasibility, regulatoryreadiness and cost of goods estimates.

Platform Selection

The assays used to identify genes in the discovery phase need not be thesame assays used to measure genes in the commercial test. For example,microarrays are an effective and comprehensive gene discovery platform.In other cases, markers are identified by real-time quantitative PCRmethods such as TaqMan or Lightcycler. Unfortunately, differences indynamic range, sensitivity and other assay parameters may result in highlevels of marker attrition, sometimes as high as 50% loss per iteration.Multiple rounds of gene selection and validation can ensue, withrepeated sampling from the original gene discovery pool.

In some embodiments, the present invention provides a method to bothdiscover genes and measure them in clinical assays on the same platform:the microarray. In some embodiments, cost can be reduced by designing acustom microarray with only a portion of the original content, yet withenough extra content to ensure that the markers required for algorithmperformance are retained, regardless of marker drop-out. The highparallelism of microarrays allows us to use the same wet laboratoryassay to measure 20,000 genes as it does 200. In some embodiments, aNuGEN amplification and Affymetrix detection system can be usedtogether.

The nature of the training set represents the biggest obstacle to thecreation of a robust classifier. Often, a given clinical cohort islimited by the prevalence of disease subtypes that are represented,and/or there is no clear phenotypic, biological, and/or moleculardistinction between disease subtypes. Theoretically, the training setcan be improved by increasing the number and nature of samples in aprospective clinical cohort, however this approach is not alwaysfeasible. Joining of multiple datasets to increase the overall size ofthe cohort used for training the classifier can be accompanied byanalytical challenges and experimental bias.

In one aspect, the present invention overcomes limitations in thecurrent art by joining multiple datasets and applying a technical factorremoval normalization approach to the datasets either prior to and/orduring classification. In another aspect, the present invention providesmethods for gene selection. In another aspect, the present inventionintroduces a novel ROC-based method for obtaining more accuratesubtype-specific classification error rates. Multiple datasets belongingto distinct experiments can be combined and analyzed together. Thisincreases the number of samples available for model training and theoverall accuracy of the predictive algorithm.

1. Quality Control

Affymetrix Power Tools (APT, version 1.10.2) software can be used toprocess, normalize and summarize output (post-hybridization) microarraydata (.CEL) files. Quantile normalization, detection above background(DABG), and robust multichip average (RMA) determination of AUC can bedone using APT, a program that has been written and streamlined for theautomatic processing of post-hybridization data. This automatedprocessing script produces a probeset-level intensity matrix and agene-level intensity matrix. DABG can be computed as the fraction ofprobes having smaller than p<1e-4 when compared with background probesof the similar GC content (Affymetrix). Accurate classification may beencumbered by a variety of technical factors including failed orsuboptimal hybridization. Post-Hybridization QC metrics can becorrelated with Pre-Hybridization QC variables to identify the technicalfactors that may obscure or bias signal intensity.

TABLE 1 Algorithm Nomenclature Algorithm Purpose Classification Analgorithm to classify thyroid samples into benign and nonbenign Combo Analgorithm to combine three analytical methods of marker extraction(LIMMA, LIMMA + Bayesian, LIMMA + Repeatability)

TABLE 2 List of standard R packages used in the molecular classifierUsed for: Package Built mva plots AffymetrixPLM 2.7.2 Used for SVM e10712.7.2 Used for LASSO logistic regression glmnet 2.9.0 Used for GSAanalysis GSA 2.9.0 Used for various plots lattice 2.9.0 Used for geneselection limma 2.7.2 Used for margin tree classifier marginTree 2.9.0Used for LDA MASS 2.9.0 Used for RF classifier and gene selectionrandomForest 2.7.2 Used for heatmaps RColorBrewer 2.7.2 Used for ROCcurve visualization ROCR 2.8.0 Used for exception handling R.oo 2.8.1Used for diagonal LDA classifier sfsmisc 2.8.1

i. Classification Version

In some embodiments, Classification is used to analyze data in thesubject method. The version of the engine used to generate reports atthe end of discovery has been tagged as Release-Classification-1.0 inSVN. In one example, the data analyzed by Classification are .CEL filesgenerated from Thyroid Tissue and FNA samples run on Affymetrix HumanExon 1 ST array with NA26 annotations.

In one example, the overall workflow after scanning the microarrays isas follows: output .CEL files→APT→Intensity Matrix→pDABG/AUC→removesamples with AUC ≤0.73 and DABG≤x→Plot PCAs of each categoricaltechnical factor→Plot variance component as function of each technicalfactor→Determine if additional samples need to be removed orflagged→Determine if factor needs removal globally or withincross-validation→run classification.

Two common QC metrics used to pass or fail a sample in order to enter itinto the molecular classifier are Intron/Exon separation AUC and pDABGor pDET. The threshold for AUC can be, for example, around 0.73. Thethreshold for pDET (percentage of genes or probe sets that are detectedabove background) can be adjusted for different data sets as learningcontinues during marker discovery.

2. Exploratory Analysis

In some embodiments, the present invention utilizes one or moreexploratory methods to generate a broad preliminary analysis of thedata. These methods are used in order to assess whether technicalfactors exist in the datasets that may bias downstream analyses. Theoutput from exploratory analyses can be used to flag any suspicioussamples, or batch effects. Flagged samples or subsets of samples canthen be processed for technical factor removal prior to, and/or duringfeature selection and classification. Technical factor removal isdescribed in detail in section 3. The methods used for exploratoryanalyses include but are not limited to:

Principal component analysis (PCA) can be used to assess the effects ofvarious technical factors, such as laboratory processing batches or FNAsample collection media, on the intensity values. To assess the effectsof technical factors, the projection of the normalized intensity valuesto the first few principal components can be visualized in a pair-wisemanner, color coded by the values of the technical variable. If asignificant number of samples are affected by any given technical factorand the first few principal components show separation according to thefactor, this factor can be considered a candidate for computationalremoval during subsequent phases of analysis.

In addition to PCA visualization, the present invention can utilizeanalysis of variance (variance components) as a quantitative measure toisolate technical factors that have significant effect on normalizedintensity values. Variance decomposition can be achieved by fitting alinear model to the normalized intensity values for each of the genesthat passes non-specific filtering criteria. The explanatory variablesin the linear model include biological factors as well as technicalfactors of interest. When categorical technical factors are representedsparsely in the data, combinations of these factors can be explored asexplanatory variables in the model to reduce the number of parametersand enable estimation of effect sizes due to individual variables. Inone embodiment, once the linear model is fitted for gene n, the omegasquared measure (ω²) is used to provide an unbiased assessment of theeffect size for each of the explanatory variables j on the individualgene (Bapat, R. B. (2000). Linear Algebra and Linear Models (Second ed.)Springer):

$\omega^{2} = \frac{\left( {{SS}_{effect} - {{df}_{effect}*{MS}_{error}}} \right)}{\left( {{MS}_{error} + {SS}_{total}} \right)}$

Here SS_(effect) is the sum of squares due to the explanatory variable,MS_(error) is the mean squared error, SS_(total) is the total sum ofsquares, and df_(effect) is the degrees of freedom associated with aparticular variable. To assess average effect size across all genespassing non-specific filtering criteria, average values of ω² arecalculated across all genes and visualized either as raw effect sizes orproportions of total variance explained. FIG. 1 shows an example plotwith average effect sizes assessed across one biological factor(pathology class) and three technical factors (one continuous andcategorical). Non-biological explanatory variables with effect sizesgreater than or comparable to the biological factors are consideredcandidates for computational removal as technical factors.

3. Technical Factor Removal

PCA and Variance components can be used to assess the magnitude andsignificance of the technical variability in the data relative to thebiological signal. If it was deemed that technical sources ofvariability must be removed, then the regression method can be used toremove that effect.

(a) Details on the regression method: In a supervised setting, thismethod can be used to adjust the probe intensities for variation due totechnical reasons (e.g. sample collection media) in the presence of theprimary variable of interest (the disease label). Adjustments fortechnical factors can be made both in gene/feature selection, as well asin feature adjustment necessary for correct classification. For example:

(I) Feature selection linear model:E(y)=β₀+β₁ BM+β ₂ TF ₁+β₃ BM*TF ₁+ . . . +ε

where TF1 is technical factor 1; and BM is the variable which containsthe label ‘B’ or ‘M’. The current call to LIMMA for feature selectionwould be extended to support the adjustment by technical factors (up to3) and corresponding 2-way interaction terms with the BM variable, ifneeded.

Feature Adjustment: The features themselves can be adjusted in thefollowing way:Y−Ŷ=Y−X{circumflex over (β)}

where {circumflex over (β)} are the estimated coefficients from theterms in the feature selection linear model equation which involvetechnical factors. In some instances, the model matrix will contain onlythe variables containing the technical factor and will not contain thecolumn of 1's (the intercept term).

In unsupervised correction, the technical factor (TF) covariate can beused to shift the means between samples of one type (e.g., banked FNA)and those of another (e.g., prospective FNA). A boxplot of all the probeintensities for each sample will show whether such a “shift in means”exists due to known factors of technical variation.

In some embodiments, only if the technical source of variability issimply a global “shift in means” or linear and is not confounded bydisease subtype then the regression method in an unsupervised settingwill be applied. This would be an unsupervised correction, i.e., nodisease labels will be used in the correction step.

In some embodiments, if evidence of technical variability is present inthe data, but biological signal overwhelms it, no correction is appliedto the data sets. A list of co-variables that can be examined by thesubject algorithm is shown in Table 3.

TABLE 3 Technical factors or variables considered in the algorithmVariable Values Collection source OR vs. Clinic Collection method BankedFNA vs. Prospective FNA Collection media Trizol vs. RNAProtect RNA RINContinuous WTA yield Continuous ST yield Continuous Hybridization siteLaboratory 1 vs. Laboratory 2 Hybridization quality (AUC) ContinuousGeneral pathology Benign vs. Malignant Subtype pathology LCT, NHP, FA,HA, FC, FVPTC, PTC, MTC Experiment batch FNA TRIzol 1-4 vs. FNARNAprotect 1-4 or FNA TRIzol vs. FNA RNAprotect Lab contaminationDominant peak, band seen, both

Classification accuracy, sensitivity, specificity, ROC curves, error vsnumber of markers curves, positive predictive value (PPV) and negativepredictive value (NPV) can be reported using these approaches. Themethods of the present invention have sensitivity required to detectrare transcripts, which are expressed at a few copies per cell, and toreproducibly detect at least approximately two-fold differences in theexpression levels. In some embodiments, the subject methods provide ahigh sensitivity of detecting gene expression and therefore detecting agenetic disorder or cancer that is greater than 60%, 65%, 70%, 75%, 80%,85%, 90%, 91%, 92%, 93%, 94%, 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%,98.5%, 99%, 99.5% or more. Therefore, the sensitivity of detecting andclassifying a genetic disorder or cancer is increased. Theclassification accuracy of the subject methods in classifying geneticdisorders or cancers can be greater than 70%, 75%, 80%, 85%, 90%, 91%,92%, 93%, 94%, 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%, 98.5%, 99%,99.5% or more. In some embodiments, the subject methods provide a highspecificity of detecting and classifying gene expression that is greaterthan, for example, 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 95.5%, 96%,96.5%, 97%, 97.5%, 98%, 98.5%, 99%, 99.5% or more. In some embodiments,the nominal specificity is greater than or equal to 70%. The nominalnegative predictive value (NPV) is greater than or equal to 95%. In someembodiments, the NPV is about 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%,98.5%, 99%, 99.5% or more.

4. Biomarker List Generation

The output of this method can be a marker list, such as a list whichconsists of the top differentially expressed genes distinguishingbetween benign and malignant. In some embodiments, the process ofderiving marker lists uses three different analytical methods. In someembodiments, the present invention utilizes feature selection inside theclassification engine, as a component of the cross-validation step (e.g.to refine the candidate genes list). Feature selection uses therepeatability criteria with the LIMMA model (Smyth 2004; Diaz-Uriarteand Alvarez de Andres 2006). The repeatability method has been describedin the context of meta-analysis of microarray data (Fishel, Kaufman etal. 2007). For a fixed set of K genes and m cross-validation folds inthe data, the selection probability of a feature is the number of foldsthat have been chosen by LIMMA as a discriminating feature between theBenign and Malignant classes divided by m. If the feature is chosen inevery fold by LIMMA then the selection probability of that feature is 1.A maximum of K features are chosen in each fold of training and test setcombination. At the end of repeatable feature selection, a union set ofeach of these distinct set of K features is taken and denoted by K′ setof combined repeatable features. A repeatability threshold is set (e.g.,0.5 denotes that features must have appeared in half of the mcross-validation folds) in order to choose the highly consistent set offeatures from cross-validation.

Since feature selection can be done in conjunction with classification,repeatable feature selection can happen in an inner loop inside thecross-validation. Once the repeatable features are selected via theinner loop, they can be used for the classifier to make a call on thetest set data of the outer cross-validation loop.

In some embodiments, feature selection (gene selection) can also becombined with technical factor removal. Often a variable, such as thesample collection media, provides a distinct shift at the intensitylevel. If the variable is associated with the disease subtype (Benign vsMalignant) then feature selection can account for this variable (aconfounder) in the regression model (LIMMA). The details of accountingfor technical factor effects on the intensities in feature selection aredescribed in Section 3 above. Repeatable feature selection can becarried out together with correction/removal of relevant technicalfactors present in the data set for each regression model applied to thegenes.

In addition to feature selection methods applied inside thecross-validation engine, more steps can be performed to output a finalgene list after cross-validation is complete. In some embodiments, thisgene list is not used on the same data set, but rather on subsequentdata sets in order to obtain an unbiased estimate of the error rate. Insome embodiments, in producing the final gene list, the following stepsare taken:

Step 1: The top ranking genes from an initial discovery data set areidentified using the LIMMA model based on significance after Benjaminiand Hochberg correction for false discovery rate (FDR). An FDR filtervalue of p<0.05 was used. The list is ranked via the FDR p-value.

Step 2: A Bayesian approach is taken by augmenting the LIMMA model.First, previously published molecular profile studies are examined inorder to derive type I and type II error rates of assigning a gene intoa “benign” or “malignant” category. The error rates are calculated basedon the sample size reported in each particular published study with anestimated fold-change value of two. Second, these prior probabilitiesare combined with the output of the Tissue dataset to estimate theposterior probability of differential gene expression. Lastly theseprobabilities are combined with the FNA dataset to formulate the finalposterior probabilities of differential expression (Smyth 2004). Theseposterior probabilities are used to rank the genes and those that exceeda posterior probability threshold of 0.9 are selected. The list is thenranked via the posterior probability index.

Step 3: The repeatable gene list from the cross-validation analysis onthe FNA data set is combined with an analogous list from the initialLIMMA only discovery data set (step 1). A joint core-set of features iscreated using the top ranked features that appear on both lists. Thelist is ranked via the repeatability index.

Step 4: The three lists (LIMMA, Bayes, and Repeatable), are combined toform a list of top markers (Combo) of size λ with λ=min(λ1+λ2+λ3), whereλi is the length of the list from each of the three methods describedherein.

Each of the steps described above can be done independently. In oneembodiment, the subject method comprises step 1. In another embodiment,the subject method comprises step 2. In another embodiment, the subjectmethod comprises step 3. In another embodiment, the subject methodcomprises step 4. In another embodiment, the subject method comprises acombination of steps 1, 2, 3, and/or 4.

5. Classification Methods

Pathology labels are the gold-standard used to characterize a givensample and these can be adjudicated after fine needle aspiration (FNA)cytology and/or post-surgical tissue cytology. A major limitation ofusing cytology to establish pathology is the arbitrary and error-pronenature of adjudicating a call. Tissues from any given organ maybecharacterized as belonging to as many as ten or more “distinct”pathology subtypes. In the present invention subtype pathology labelsare sometimes used to train the molecular classifier, but more often avariety of pathology subtypes are grouped together into a binarycategory: benign or malignant.

In one embodiment, data are from 10 prevalent thyroid subtypes that arepresent in the Tissue and FNA (Fine needle aspirate) samples. Thesubtypes include but are not limited to LCT, NHP, FA, HA (these 4subtypes are benign) and the remaining subtypes are FC, HC, PTC, FVPTC,ATC, MTC (these 6 subtypes are malignant). The classifier separates thesamples into benign or malignant.

In some embodiments, simplified pathology labels (benign or malignant)adjudicated to each sample by a trained pathologist are used to trainthe molecular classifier. However, it is recognized that pathologylabels are often incorrect and their nomenclature may not necessarilydescribe the true nature of the pathology that actually exists in thesampled tissue. For example in thyroid cytology, nodular hyperplasias(NHP, a benign pathology) are relatively simple to distinguish frompapillary carcinomas (PTC, a malignant pathology), while follicularadenomas (FA, a debated benign condition) are much harder to distinguishfrom follicular carcinomas (FC, a malignant pathology). In fact, theadjudication of an FA versus an FC pathology label maybe completelyarbitrary and artificial. It has been argued that FA is simply theearliest recognizable stage of all eventual FCs (Tzen, Huang et al.2003; Gombos, Zele et al. 2007). Cases of thyroid FA metastasis to otherorgans have been reported many years after complete thyroidectomy andpathology adjudication were performed, highlighting the severity of theproblem (Kashigina, Girshin et al. 1980; Mizukami, Nonomura et al. 1996;Baloch and LiVolsi 2007; Ito, Yabuta et al. 2008; Tadashi 2009). Thus,while pathology labels remain the gold-standard in pathology diagnosis,the present invention overcomes some of its limitations by complementingtraining of the molecular classification algorithm with empirical datagathered during unsupervised analyses.

One goal of the molecular classifier is to separate all prevalentdisease subtypes from any combination of sample cohorts (e.g. experiment1+experiment 2) into a binary category: benign or malignant. A number ofpredictive modeling algorithms and or statistical methods are used toclassify a given set of biomarkers to produce this binary class labelper sample. The current method provides a modular molecular classifieruseable in 1) “cross-validation mode”, 2) “split-sample mode”, and/or 3)“save model & predict mode”. The framework of the molecular classifieris sufficiently flexible such that a variety of classification methodscan be easily added and implemented in any mode. This feature of themolecular classifier allows the user to estimate classificationperformance and to obtain model parameters that improve classification.Classification methods include, but are not limited to, support vectormachines (SVM), linear discriminant analysis (LDA, diagonal or pooled),K-nearest neighbor analysis (KNN), random forest (RF), lassoed logisticregress, MarginTree, Rulefit, Sum(UP regulated markers)-sum(DOWNregulated markers) etc.

(a) Cross-Validation Mode:

In cross-validation mode at least two methods can be used, either one ata time, or in succession. The cross-validation methods are K-foldcross-validation and leave-one-out cross-validation (LOOCV). In oneembodiment, feature selection (with or without technical factor removal)is incorporated within each loop of cross-validation. This enables theuser to obtain unbiased estimates of error rates. Further, featureselection can also be performed within certain classifiers (e.g., randomforest) in a multivariate setting. The classifier takes the featuresselected and the previously built training-set model and makes aclassification call (Benign or not Benign) on the test set. Thisprocedure of repeatedly splitting the data into training and test setsand providing a single averaged error rate at the end gives an unbiasederror rate in the cross-validation mode.

(b) Split Sample Mode

In split sample mode, samples can be split into a single training setand a single test (validation) set. The training set is used to obtain amodel and its parameters. The validation set is used to obtain ageneralized error estimate. There are two variations of the split sampleprocessing procedure in our framework:

Training and validation data sets can be normalized and processedtogether (APT, RMA with quantile normalization) including removal oftechnical factors when necessary. The data can be split into trainingand validation sets based on specific criteria (e.g., balancing each setby relevant covariate levels).

The training set can be normalized and summarized first; then thevalidation set can be normalized to the APT sketch of the training setand summarized using the RMA of the training set and each additionalvalidation sample. For example, if there are n test samples, this can bedone n times. After RMA summarization, the datasets can be extracted andcombined into one data set.

(c) Save Model and Predict Mode (Aka “Save and Predict”)

This mode is similar to split sample mode except that the model and itsparameters are generated and saved using an entire dataset, instead ofsplitting the data sets into training and validation sets. Thevalidation set is provided externally.

6. Accuracy Determination Methods

In all three modes of model training described above, classificationperformance measures can include but are not limited to error rates,area under receiver operating characteristic (ROC) curve, false positiverate, false negative rate, PPV/NPV, etc. Two general aspects apply toall performance measures.

First, due to heterogeneity of the data sets with respect to pathologysubtypes, performance varies by subtype and each measure is reported forindividual subtypes. Second, since training data sets are notnecessarily collected prospectively, training set prevalence ofpathology subtypes is not reflective of the population prevalence.Accurate assessment of classification performance requires adjustingperformance measures to their expected values given population pathologysubtype prevalence.

(a) ROC

Receiver operator characteristic (ROC) curves can be used to visualizethe trade-off between sensitivity and specificity (false negative/falsepositive errors). ROC curves can be generated both for training setdisease subtype prevalence and population disease subtype prevalence.

The algorithms of the present invention (grey trace) demonstrate thatcurrent art methods (black trace) incorrectly assess classificationperformance (FIG. 4A). These errors in classification are highlightedwhen distinct pathology subtypes are probed independently of all othersubtypes and re-sampled to generate a second—subtype specific—ROC curveof the data (FIG. 4B). The present invention improves the accuracy ofclassification performance calculation methods.

(b) Optimal Error Rate

Error rates of the optimal decision rule can be generated both fortraining set disease subtype prevalence and population disease subtypeprevalence.

(c) PPV/NPV

Sensitivity/specificity of the classifier at a given decision thresholdare converted to NPV/PPV using a range of values for the prevalence ofmalignancy. NPV/PPV values are generated both for training set diseasesubtype prevalence and population disease subtype prevalence.

(d) False Positive Error Rate Given False Negative Error Rate

Since the clinical diagnostic test can only tolerate a small number offalse negative results, in some embodiments, classifiers are evaluatedby comparing their false positive rates at an acceptable false negativethreshold.

(e) Subtype Specific Error Rate

Error rates of the optimal decision rule are reported by diseasesubtype.

(f) Error Rates in Split-Sample Mode.

In some embodiments, two approaches are taken to quantify variability ofthe split sample estimate and evaluate the significance of its value:

(i) Generate a number of n=100 data sets of similar subtype make-up forsplit-sample training/testing using tissue data; run split sample modeanalysis through classification for a single predefined number ofmarkers (for example, 100); estimate the variance of error rate observedfor the test set in each of 100 random splits. The distribution of errorrate from this simulation is used to estimate error bars and confidenceintervals for the achieved error.

(ii) In addition, the significance of achieved error rates can beevaluated against the null hypothesis classification all samples intothe majority class. Let err be the achieved error rate, then thesignificance is the probability of observing err using baselineclassifier that assigns everything to the majority class. The p-valuefor significance is the probability that pathology subtypes will begenerated by re-sampling errors achieved on available samples inrelevant proportions.

The estimates of metrics described in section 6 can be reported for eachdata set, as well as adjusted by re-sampling subtype prevalence to thatpresent in any given pathology subtype and reported again.

In some embodiments, the exon array platform used in the presentinvention measures mRNA levels of all known human genes (˜24,000) andall known transcripts (>200,000). This array is used on every sample runin feasibility (i.e. gene discovery), therefore the algorithm is trainedon the full complement of genes at every step. Throughout algorithmtraining, feature (i.e. gene) selection occurs de novo for everyexperimental set. Thus, features may be selected from multipleexperiments and later combined.

Marker panels can be chosen to accommodate adequate separation of benignfrom non-benign expression profiles. Training of this multi-dimensionalclassifier, i.e., algorithm, was performed on over 500 thyroid samples,including >300 thyroid FNAs. Many training/test sets were used todevelop the preliminary algorithm. First the overall algorithm errorrate is shown as a function of gene number for benign vs non-benignsamples. All results are obtained using a support vector machine modelwhich is trained and tested in a cross-validated mode (30-fold) on thesamples.

In some embodiments, the difference in gene expression level is at least10%, 15%, 20%, 25%, 30%, 35%, 40%, 45% or 50% or more. In someembodiments, the difference in gene expression level is at least 2, 3,4, 5, 6, 7, 8, 9, 10 fold or more. In some embodiments, the biologicalsample is identified as cancerous with an accuracy of greater than 75%,80%, 85%, 90%, 95%, 99% or more. In some embodiments, the biologicalsample is identified as cancerous with a sensitivity of greater than95%. In some embodiments, the biological sample is identified ascancerous with a specificity of greater than 95%. In some embodiments,the biological sample is identified as cancerous with a sensitivity ofgreater than 95% and a specificity of greater than 95%. In someembodiments, the accuracy is calculated using a trained algorithm.

In some embodiments of the present invention, molecular profilingincludes the step of binding the sample or a portion of the sample toone or more probes of the present invention. Suitable probes bind tocomponents of the sample, i.e. gene products, that are to be measuredand include but are not limited to antibodies or antibody fragments,aptamers, nucleic acids, and oligonucleotides. The method of diagnosingcancer based on molecular profiling further comprises the steps ofdetecting gene expression products (i.e. mRNA or protein) and levels ofthe sample, comparing it to an amount in a normal control sample todetermine the differential gene expression product level between thesample and the control; and classifying the test sample by inputting oneor more differential gene expression product levels to a trainedalgorithm of the present invention; validating the sample classificationusing the selection and classification algorithms of the presentinvention; and identifying the sample as positive for a genetic disorderor a type of cancer.

(i) Gene Expression Products and Splice Variants of the PresentInvention

Molecular profiling may also include but is not limited to assays of thepresent disclosure including assays for one or more of the following:protein expression products, DNA polymorphisms, RNA expression products,RNA expression product levels, or RNA expression product splice variantsof the genes. In some cases, the methods of the present inventionprovide for improved cancer diagnostics by molecular profiling of about1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70,80, 90, 100, 120, 140, 160, 180, 200, 240, 280, 300, 350, 400, 450, 500,600, 700, 800, 1000, 1500, 2000, 2500, 3000, 3500, 4000, 5000 or moregene product expression markers and/or alternative splice variantmarkers.

In one embodiment, molecular profiling involves microarray hybridizationthat is performed to determine gene expression product levels for one ormore genes selected from FIG. 5, FIG. 6, FIG. 7, FIG. 8, or FIG. 9 ofU.S. Provisional application Ser. No. 61/199,585, and PCT applicationUS2009/06162, which are hereby incorporated by reference in theirentirety. In some cases, gene expression product levels of one or moregenes from one group are compared to gene expression product levels ofone or more genes in another group or groups. As an example only andwithout limitation, the expression level of gene TPO may be compared tothe expression level of gene GAPDH. In another embodiment, geneexpression levels are determined for one or more genes involved in oneor more of the following metabolic or signaling pathways: thyroidhormone production and/or release, protein kinase signaling pathways,lipid kinase signaling pathways, and cyclins. In some cases, the methodsof the present invention provide for analysis of gene expression productlevels and or alternative exon usage of at least one gene of 1, 2, 3, 4,5, 6, 7, 9, 10, 11, 12, 13, 14, or 15 or more different metabolic orsignaling pathways.

(ii) Comparison of Sample to Normal

The results of the molecular profiling performed by the molecularprofiling on the sample provided by the individual (test sample) may becompared to a biological sample that is known to be normal. A normalsample is that which is or is expected to be free of any cancer, diseasesuch as a genetic disease, or condition, or a sample that would testnegative for any cancer disease or condition in the molecular profilingassay. The normal sample may be from a different individual from theindividual being tested, or from the same individual. The normal samplemay be assayed at the same time, or at a different time from the testsample.

The results of an assay on the test sample may be compared to theresults of the same assay on a normal sample. In some cases the resultsof the assay on the normal sample are from a database, or a reference.In some cases, the results of the assay on the normal sample are a knownor generally accepted value by those skilled in the art. In some casesthe comparison is qualitative. In other cases the comparison isquantitative. In some cases, qualitative or quantitative comparisons mayinvolve but are not limited to one or more of the following: comparingfluorescence values, spot intensities, absorbance values,chemiluminescent signals, histograms, critical threshold values,statistical significance values, gene product expression levels, geneproduct expression level changes, alternative exon usage, changes inalternative exon usage, or nucleic acid sequences.

(iii) Evaluation of Results

In some embodiments, the molecular profiling results are evaluated usingmethods known to the art for correlating gene product expression levelsor alternative exon usage with specific phenotypes such as malignancy,the type of malignancy (e.g. follicular carcinoma), or benignancy. Insome cases, a specified statistical confidence level may be determinedin order to provide a diagnostic confidence level. For example, it maybe determined that a confidence level of greater than 90% may be auseful predictor of malignancy, type of malignancy, or benignancy. Inother embodiments, more stringent or looser confidence levels may bechosen. For example, a confidence level of approximately 70%, 75%, 80%,85%, 90%, 95%, 97.5%, 99%, 99.5%, or 99.9% may be chosen as a usefulphenotypic predictor. The confidence level provided may in some cases berelated to the quality of the sample, the quality of the data, thequality of the analysis, the specific methods used, and the number ofgene expression products analyzed. The specified confidence level forproviding a diagnosis may be chosen on the basis of the expected numberof false positives or false negatives and/or cost. Methods for choosingparameters for achieving a specified confidence level or for identifyingmarkers with diagnostic power include but are not limited to ReceiverOperator Curve analysis (ROC), binormal ROC, principal componentanalysis, partial least squares analysis, singular value decomposition,least absolute shrinkage and selection operator analysis, least angleregression, and the threshold gradient directed regularization method.

(iv) Data Analysis

Raw gene expression level and alternative splicing data may in somecases be improved through the application of algorithms designed tonormalize and or improve the reliability of the data. In someembodiments of the present invention the data analysis requires acomputer or other device, machine or apparatus for application of thevarious algorithms described herein due to the large number ofindividual data points that are processed. A “machine learningalgorithm” refers to a computational-based prediction methodology, alsoknown to persons skilled in the art as a “classifier”, employed forcharacterizing a gene expression profile. The signals corresponding tocertain expression levels, which are obtained by, e.g., microarray-basedhybridization assays, are typically subjected to the algorithm in orderto classify the expression profile. Supervised learning generallyinvolves “training” a classifier to recognize the distinctions amongclasses and then “testing” the accuracy of the classifier on anindependent test set. For new, unknown samples the classifier can beused to predict the class in which the samples belong.

In some cases, the robust multi-array Average (RMA) method may be usedto normalize the raw data. The RMA method begins by computingbackground-corrected intensities for each matched cell on a number ofmicroarrays. The background corrected values are restricted to positivevalues as described by Irizarry et al. Biostatistics 2003 Apr. 4 (2):249-64. After background correction, the base-2 logarithm of eachbackground corrected matched-cell intensity is then obtained. Theback-ground corrected, log-transformed, matched intensity on eachmicroarray is then normalized using the quantile normalization method inwhich for each input array and each probe expression value, the arraypercentile probe value is replaced with the average of all arraypercentile points, this method is more completely described by Bolstadet al. Bioinformatics 2003. Following quantile normalization, thenormalized data may then be fit to a linear model to obtain anexpression measure for each probe on each microarray. Tukey's medianpolish algorithm (Tukey, J. W., Exploratory Data Analysis. 1977) maythen be used to determine the log-scale expression level for thenormalized probe set data.

Gene expression data may further be filtered to remove data that may beconsidered suspect. In some embodiments, data deriving from microarrayprobes that have fewer than about 4, 5, 6, 7 or 8 guanosine+cytosinenucleotides may be considered to be unreliable due to their aberranthybridization propensity or secondary structure issues. Similarly, dataderiving from microarray probes that have more than about 12, 13, 14,15, 16, 17, 18, 19, 20, 21, or 22 guanosine+cytosine nucleotides may beconsidered unreliable due to their aberrant hybridization propensity orsecondary structure issues.

In some cases, unreliable probe sets may be selected for exclusion fromdata analysis by ranking probe-set reliability against a series ofreference datasets. For example, RefSeq or Ensembl (EMBL) are consideredvery high quality reference datasets. Data from probe sets matchingRefSeq or Ensembl sequences may in some cases be specifically includedin gene expression analysis experiments due to their expected highreliability. Similarly data from probe-sets matching less reliablereference datasets may be excluded from further analysis, or consideredon a case by case basis for inclusion. In some cases, the Ensembl highthroughput cDNA (HTC) and/or mRNA reference datasets may be used todetermine the probe-set reliability separately or together. In othercases, probe-set reliability may be ranked. For example, probes and/orprobe-sets that match perfectly to all reference datasets such as forexample RefSeq, HTC, and mRNA, may be ranked as most reliable (1).Furthermore, probes and/or probe-sets that match two out of threereference datasets may be ranked as next most reliable (2), probesand/or probe-sets that match one out of three reference datasets may beranked next (3) and probes and/or probe sets that match no referencedatasets may be ranked last (4). Probes and or probe-sets may then beincluded or excluded from analysis based on their ranking. For example,one may choose to include data from category 1, 2, 3, and 4 probe-sets;category 1, 2, and 3 probe-sets; category 1 and 2 probe-sets; orcategory 1 probe-sets for further analysis. In another example,probe-sets may be ranked by the number of base pair mismatches toreference dataset entries. It is understood that there are many methodsunderstood in the art for assessing the reliability of a given probeand/or probe-set for molecular profiling and the methods of the presentinvention encompass any of these methods and combinations thereof.

In some embodiments of the present invention, data from probe-sets maybe excluded from analysis if they are not expressed or expressed at anundetectable level (not above background). A probe-set is judged to beexpressed above background if for any group:

-   -   Integral from T0 to Infinity of the standard normal        distribution<Significance (0.01)    -   Where:        T0=Sqr(GroupSize)(T−P)/Sqr(Pvar),    -   GroupSize=Number of CEL files in the group,    -   T=Average of probe scores in probe-set,    -   P=Average of Background probes averages of GC content, and    -   Pvar=Sum of Background probe variances/(Number of probes in        probe-set){circumflex over ( )}2,

This allows including probe-sets in which the average of probe-sets in agroup is greater than the average expression of background probes ofsimilar GC content as the probe-set probes as the center of backgroundfor the probe-set and enables one to derive the probe-set dispersionfrom the background probe-set variance.

In some embodiments of the present invention, probe-sets that exhibitno, or low variance may be excluded from further analysis. Low-varianceprobe-sets are excluded from the analysis via a Chi-Square test. Aprobe-set is considered to be low-variance if its transformed varianceis to the left of the 99 percent confidence interval of the Chi-Squareddistribution with (N−1) degrees of freedom.(N−1)*Probe-set Variance/(Gene Probe-set Variance)˜Chi-Sq(N−1)

where N is the number of input CEL files, (N−1) is the degrees offreedom for the Chi-Squared distribution, and the ‘probe-set variancefor the gene’ is the average of probe-set variances across the gene.

In some embodiments of the present invention, probe-sets for a givengene or transcript cluster may be excluded from further analysis if theycontain less than a minimum number of probes that pass through thepreviously described filter steps for GC content, reliability, varianceand the like. For example in some embodiments, probe-sets for a givengene or transcript cluster may be excluded from further analysis if theycontain less than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, or less than about 20 probes.

Methods of data analysis of gene expression levels and or of alternativesplicing may further include the use of a pre-classifier algorithm. Forexample, fine needle aspirates (FNAs) of thyroid nodules contain severalcell types, including thyroid follicular cells, thyroid medullary cells,blood cells (RBCs, WBCs, platelets), smooth muscle cells andinfiltrating macrophages and lymphocytes. Diagnostic classification ofFNAs involves primarily follicular cells (for cancers derived from thefollicular cell such as papillary carcinoma, follicular carcinoma, andanaplastic thyroid carcinoma) and medullary cells (for medullarycancer). Since medullary and anaplastic thyroid cancers are rarelypresent in the indeterminate class, the diagnosis of indeterminate FNAsmainly concerns the distinction of follicular adenoma versus follicularcarcinoma. The gene expression signal of the follicular cell is thusdiluted out and possibly confounded by other cell types present in theFNA. An upfront method of determining the cellular make-up of aparticular FNA may allow the resulting gene expression signatures to becalibrated against the dilution effect. A combination of knowncell-specific genes may be used as an upfront mini-classifier for eachcell component of the FNA. An algorithm may then use this cell-specificmolecular fingerprint, pre-classify the samples according to theircomposition and then apply a correction/normalization factor. Thisdata/information may then be fed in to a final classification algorithmwhich would incorporate that information to aid in the final diagnosisof Benign or Normal versus Malignant.

Genetic disorder or cancer diagnoses can be performed by comparing thelevels of expression for a marker gene or a set of marker genes in atest sample, for example, a neoplastic cell sample to the levels ofexpression for a marker gene or a set of marker genes in a normal cellsample of the same tissue type. Alternatively, the level of expressionfor a marker gene or a set of marker genes in a cell sample is comparedto a reference pool of RNA that represents the level of expression for amarker gene or a set of marker genes in a normal population (hereintermed “training set”). The training set also includes the data for apopulation that has a known tumor or class of tumors. This datarepresents the average level of expression that has been determined forthe neoplastic cells isolated from the tumor or class of tumors. It alsohas data related to the average level of expression for a marker gene orset of marker genes for normal cells of the same cell type within apopulation, in these embodiments, the algorithm compares newly generatedexpression data for a particular marker gene or set of marker genes froma cell sample isolated from a patient containing potentially neoplasticcells to the levels of expression for the same marker gene or set ofmarker genes in the training set. The algorithm determines whether acell sample is neoplastic or normal by aligning the level of expressionfor a marker gene or set of marker genes with the appropriate group inthe training set.

A statistical evaluation of the results of the molecular profiling mayprovide a quantitative value or values indicative of one or more of thefollowing: the likelihood of diagnostic accuracy, the likelihood ofcancer, disease or condition, the likelihood of a particular cancer,disease or condition, the likelihood of the success of a particulartherapeutic intervention. Thus a physician, who is not likely to betrained in genetics or molecular biology, need not understand the rawdata. Rather, the data is presented directly to the physician in itsmost useful form to guide patient care. The results of the molecularprofiling can be statistically evaluated using a number of methods knownto the art including, but not limited to: the students T test, the twosided T test, pearson rank sum analysis, hidden markov model analysis,analysis of q-q plots, principal component analysis, one way ANOVA, twoway ANOVA, LIMMA and the like.

In some embodiments of the present invention, the use of molecularprofiling alone or in combination with cytological analysis may providea diagnosis that is between about 85% accurate and about 99% or about100% accurate. In some cases, the molecular profiling business maythrough the use of molecular profiling and/or cytology provide adiagnosis of malignant, benign, or normal that is about 80%, 81%, 82%,83%, 84%, 85%, 86%, 87%, 88%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,97.5%, 98%, 98.5%, 99%, 99.5%, 99.75%, 99.8%, 99.85%, or 99.9% accurate.

In some cases, accuracy may be determined by tracking the subject overtime to determine the accuracy of the original diagnosis. In othercases, accuracy may be established in a deterministic manner or usingstatistical methods. For example, receiver operator characteristic (ROC)analysis may be used to determine the optimal assay parameters toachieve a specific level of accuracy, specificity, positive predictivevalue, negative predictive value, and/or false discovery rate. Methodsfor using ROC analysis in cancer diagnosis are known in the art and havebeen described for example in US Patent Application No. 2006/019615herein incorporated by reference in its entirety.

In some embodiments of the present invention, gene expression productsand compositions of nucleotides encoding for such products which aredetermined to exhibit the greatest difference in expression level or thegreatest difference in alternative splicing between benign and normal,benign and malignant, or malignant and normal may be chosen for use asmolecular profiling reagents of the present invention. Such geneexpression products may be particularly useful by providing a widerdynamic range, greater signal to noise, improved diagnostic power, lowerlikelihood of false positives or false negative, or a greaterstatistical confidence level.

In other embodiments of the present invention, the use of molecularprofiling alone or in combination with cytological analysis may reducethe number of samples scored as non-diagnostic by about 100%, 99%, 95%,90%, 80%, 75%, 70%, 65%, or about 60% when compared to the use ofstandard cytological techniques known to the art. In some cases, themethods of the present invention may reduce the number of samples scoredas intermediate or suspicious by about 100%, 99%, 98%, 97%, 95%, 90%,85%, 80%, 75%, 70%, 65%, or about 60%, when compared to the standardcytological methods used in the art.

In some cases the results of the molecular profiling assays, are enteredinto a database for access by representatives or agents of the molecularprofiling business, the individual, a medical provider, or insuranceprovider. In some cases assay results include interpretation ordiagnosis by a representative, agent or consultant of the business, suchas a medical professional. In other cases, a computer or algorithmicanalysis of the data is provided automatically. In some cases themolecular profiling business may bill the individual, insuranceprovider, medical provider, researcher, or government entity for one ormore of the following: molecular profiling assays performed, consultingservices, data analysis, reporting of results, or database access.

In some embodiments of the present invention, the results of themolecular profiling are presented as a report on a computer screen or asa paper record. In some cases, the report may include, but is notlimited to, such information as one or more of the following: the numberof genes differentially expressed, the suitability of the originalsample, the number of genes showing differential alternative splicing, adiagnosis, a statistical confidence for the diagnosis, the likelihood ofcancer or malignancy, and indicated therapies.

(v) Categorization of Samples Based on Molecular Profiling Results

The results of the molecular profiling may be classified into one of thefollowing: negative (free of a cancer, disease, or condition),diagnostic (positive diagnosis for a cancer, disease, or condition),indeterminate or suspicious (suggestive of a cancer, disease, orcondition), or non diagnostic (providing inadequate informationconcerning the presence or absence of a cancer, disease, or condition).In some cases, a diagnostic result may classify whether a subject has agenetic disorder. In some cases, a diagnostic result may furtherclassify the type of cancer, disease or condition. In other cases, adiagnostic result may indicate a certain molecular pathway involved inthe cancer disease or condition, or a certain grade or stage of aparticular cancer disease or condition. In still other cases adiagnostic result may inform an appropriate therapeutic intervention,such as a specific drug regimen like a kinase inhibitor such as gleevacor any drug known to the art, or a surgical intervention like athyroidectomy or a hemithyroidectomy.

In some embodiments of the present invention, results are classifiedusing a trained algorithm. Trained algorithms of the present inventioninclude algorithms that have been developed using a reference set ofknown malignant, benign, and normal samples. The classification schemeusing the algorithms of the present invention is shown in FIG. 1.Algorithms suitable for categorization of samples include but are notlimited to k-nearest neighbor algorithms, concept vector algorithms,naive bayesian algorithms, neural network algorithms, hidden markovmodel algorithms, genetic algorithms, and mutual information featureselection algorithms or any combination thereof. In some cases, trainedalgorithms of the present invention may incorporate data other than geneexpression or alternative splicing data such as but not limited toscoring or diagnosis by cytologists or pathologists of the presentinvention, information provided by the pre-classifier algorithm of thepresent invention, or information about the medical history of thesubject of the present invention.

(vi) Monitoring of Subjects or Therapeutic Interventions Via MolecularProfiling

In some embodiments, a subject may be monitored using methods andcompositions of the present invention. For example, a subject may bediagnosed with cancer or a genetic disorder. This initial diagnosis mayor may not involve the use of molecular profiling. The subject may beprescribed a therapeutic intervention such as a thyroidectomy for asubject suspected of having thyroid cancer. The results of thetherapeutic intervention may be monitored on an ongoing basis bymolecular profiling to detect the efficacy of the therapeuticintervention. In another example, a subject may be diagnosed with abenign tumor or a precancerous lesion or nodule, and the tumor, nodule,or lesion may be monitored on an ongoing basis by molecular profiling todetect any changes in the state of the tumor or lesion.

Molecular profiling may also be used to ascertain the potential efficacyof a specific therapeutic intervention prior to administering to asubject. For example, a subject may be diagnosed with cancer. Molecularprofiling may indicate the upregulation of a gene expression productknown to be involved in cancer malignancy, such as for example the RASoncogene. A tumor sample may be obtained and cultured in vitro usingmethods known to the art. The application of various inhibitors of theaberrantly activated or dysregulated pathway, or drugs known to inhibitthe activity of the pathway may then be tested against the tumor cellline for growth inhibition. Molecular profiling may also be used tomonitor the effect of these inhibitors on for example down-streamtargets of the implicated pathway.

(viii) Molecular Profiling as a Research Tool

In some embodiments, molecular profiling may be used as a research toolto identify new markers for diagnosis of suspected tumors; to monitorthe effect of drugs or candidate drugs on biological samples such astumor cells, cell lines, tissues, or organisms; or to uncover newpathways for oncogenesis and/or tumor suppression.

EXAMPLES Example 1: Biological Sample Processing

All tissues and FNAs were collected under IRB approved protocols.

Tissue Procurement

Prior to obtaining FNAs from prospective collections, thyroid surgicaltissue samples can be purchased from a clinical tissue vendor. In someembodiments, the samples are selected from their proprietary tissuedatabase, RNA is extracted by the laboratory, 50 ng amplified using theNuGEN Pico kit, and hybridized to exon arrays.

Sample Collection, Preservation and Transport

The FNA needed to be collected into a preservative that allows for thestabilization of DNA and RNA immediately following collection in theclinic, as well as through subsequent shipping and handling. In someembodiments, the present method identifies a collection medium thatallows isolation of both DNA and RNA from the FNA samples.

A study to compare different collection media is performed. In someembodiments, FNAs collected from post operative thyroid tissue arecollected into each collection media. FNAs from five individual thyroidswere collected into Trizol (Invitrogen), RNAlater (Ambion), RNAprotect(Qiagen) and the cytology medium PreservCyt (Cytyc). RNA and DNA werepurified from each of these FNAs using the Qiagen Allprep DNA/RNA micromethod and the Trizol RNA purification protocol for Trizol samples.Overall, samples collected in RNAprotect produced the highest yield andquality of both RNA and DNA and therefore RNAprotect was utilized forall future studies. For all prospective FNA studies, the FNA wascollected into 1.25 ml of RNAprotect, frozen at −80° C. and shipped toVeracyte on dry ice.

Non-Frozen Shipping and Stability Study

The aim of this study was to investigate the stability of FNAs inRNAprotect when shipped at 2-8° C. and the effects of freezing onnucleic acid yield and quality data on the stability of FNAs inRNAprotect.

To minimize effects of temperature variability in this study, sampleswere transported in shipping containers validated to keep the sample at2-8° C. for 24 hrs (Thermosafe/Fisher 03-525-054). FNAs collected onMonday through Thursday were transferred to shipping containers andshipped overnight to Veracyte. FNAs collected on Friday were stored overthe weekend at 4° C. and shipped to Veracyte on Monday alongside withsamples collected on Monday. Upon arrival the samples were split intothree groups:

-   1. Upon receipt, samples will be centrifuged and the pellet frozen    on dry ice and stored at −80° C. for later purification (n=26).-   2. Upon receipt, samples will be centrifuged and the pellet    immediately used for nucleic acid purification (n=29).-   3. Upon receipt, samples will be immediately frozen on dry ice and    stored at −80° C. for later purification (n=25).

RNA and DNA were purified from each of these FNAs using the QiagenAllprep DNA/RNA micro method. Metrics for RNA and DNA yield werecollected. These data were compared with that from samples previouslycollected from the same site using the original sample collectionprotocol (Group 0). For RNA, no significant difference was observed inyield, or in 260/280 and 260/230 ratios between groups. RIN value wasalso not altered between Group 0 and Groups 2 and 3. However, Group 1(pelleted cells) did show a reduction in RIN value. Samples taken onFriday, stored at 4° C. over the weekend and then shipped overnightshowed no difference in yield or quality to those shipped overnight.This study therefore indicates that samples can be shipped non-frozen at2-8° C. and that samples are stable at this temperature out to 72 hrs.

Sample Preparation/Extraction

We desired that nucleic acids be extracted from the FNAs using simplemolecular biology methods and preferably using commercially availablekits. Qiagen All-Prep method was used as the method e for RNA/DNAextraction and purification. Additionally, the RNA purification protocolwithin the Allprep process was modified, following Qiagen's optionalprotocol, to capture microRNA within the total RNA population. Furtheroptimization of this method was carried out to show that an 18 ulelution volume gave the best combination of yield and sampleconcentration. In some embodiments, TRIzol extraction of samples wasused. From the majority of patients from which a FNA was taken, a buccalDNA swab was also collected.

Nucleic Acid Quantification

A number of techniques for RNA quantification are available. Both theNanodrop spectrophotometer (Thermo Scientific) and the Bioanalyzer(Agilent) have been used. In later experiments the fluorescent basedmethod, Quant-IT RNA (Invitrogen), has been tested. DNA quantificationhas been carried out using the Nanodrop spectrophotometer and PicoGreenfluorescent based method (Invitrogen) with quality checked by gelelectrophoresis. Quantification data and sample usage is captured in RNAand DNA master spreadsheets.

With RNA of high concentrations, both Nanodrop and Bioanalyzer quantifyRNA accurately. However, at low concentrations of RNA and with thepresence of contaminants in the sample that absorb at 230 nM, theagreement between Nanodrop and Bioanalyzer decreases. In someembodiments, the subject method provides a fluorescent based method,Quant IT RNA (Invitrogen), to aid quantification of these samples.

NuGEN Amplification

The low RNA yields from thyroid FNAs necessitates amplification of thematerial for downstream microarray analysis. Two NuGEN protocols havebeen considered, the Pico kit and the FFPE kit. The former is specifiedfor very low starting amounts and the latter is designed for degradedRNA samples from paraffin-embedded samples. Given that the samples areboth low yield and are degraded, we tested which kit would be moresuitable for clinical FNA samples.

The WT Ovation FFPE RNA amplification system V2 is a fast and simplemethod for preparing amplified cDNA from total RNA of low quality forgene expression studies. Amplification is initiated at the 3′ end andrandomly throughout the whole transcriptome in the sample enabling theamplification of degraded RNA. When starting with 50 ng of severelydegraded RNA (such as FFPE derived material), this system can generatemicrogram quantities of cDNA. With higher quality RNA, lower inputamounts can be used to generate the required amounts of cDNA foranalysis on GeneChip arrays. A linear isothermal DNA amplificationprocess, termed Ribo-SPIA amplification, is at the heart of thisprocess. The WT Ovation Pico system is very similar to the WT OvationFFPE system, with both using the SPIA amplification process. The Picokit (AI-11) is designed for amplifying small amounts of RNA but has notbeen optimized for use with degraded RNA.

Labeled Sense strand cDNA is recommended for the Affymetrix GeneChipExon 1.0 ST array. Therefore anti-sense cDNA from the SPIA amplificationis used as a template in WT-Ovation Exon module to generate sense strandcDNA. This is fragmented and labeled using the FL-Ovation cDNA biotinmodule. Intermediates generated in this process are evaluated forquality and quantity. This information is captured in the associatedworkbook and utilized in subsequent downstream analysis.

When running the NuGEN WT-Ovation FFPE process a no template control(NTC) is routinely run. Due to the high level of amplification presentin this system, the NTC is not empty but generally contains <3 ug ofmaterial. To avoid contamination, a thorough clean of the entire labarea was carried out. All opened consumables (including plastics andreagents) were disposed of and cleaning of lab benches, shelves,cupboards and equipment with 10% bleach, DNA away, RNAse Zap and 70%EtOH was performed. Floors were also cleaned with 10% bleach, as well asa general cleaning product. A strict workflow that separates pre- andpost-PCR steps has been introduced to prevent the generation and spreadof this PCR amplicon. Subsequent to this clean up, Glory plates 5, 6, 4,and 7 were run with no evidence of amplicon contamination in thesamples.

Affymetrix Exon Array

The Affymetrix GeneChip Exon 1.0 ST array is designed with probes acrossthe length of an mRNA transcript enabling expression profiling at boththe gene and the exon level (AI-12). Hybridization controls are added(GeneChip Hybridization Control kit, Affymetrix) and the samplehybridized to the GeneChip followed by washing, staining and scanning(using the GeneChip HWS Kit on GeneChip Fluidics Station 450 andGeneChip scanner 3000 7G).

Control RNA

In some embodiments, a control RNA is utilized. This universal human RNA(UHR) purchased from Stratagene is a mixture of 10 cell lines fromdifferent tissues and is detailed in AI-7.

Illumina System

The Ilumina whole genome DASL assay was outsourced to ExpressionAnalysis and run using 100 ng total RNA following their standardprotocol.

Nanostring nCounter System

The Nanostring nCounter assay was outsourced to Nanostring and run using200 ng of total RNA following their standard protocol.

Sample Details for Discovery Datasets

Tissue—thyroid surgical tissue samples were purchased from a clinicaltissue vendor. The samples were selected from their proprietary tissuedatabase, RNA was extracted by a laboratory services vendor, 50 ngamplified using the NuGEN Pico kit, and hybridized to exon arrays.

FNA in TRIzol—This study on banked and prospective FNAs collected intoboth RNAprotect and TRIzol was split into two arms. Forty-eight sampleswere run by a laboratory services vendor using a 50 ng input (asdetermined by Nanodrop) into the NuGEN FFPE process and hybridized toexon arrays. The second arm consisted of 102 samples (2 batches), ofwhich 12 were duplicates from arm 1. These samples were run internally,using an input of 25 ng (as determined by Nanodrop) into the NuGEN FFPEprocess and hybridized to exon arrays.

FNA in RNAProtect—This study on predominantly prospective FNAs collectedinto RNAprotect was run internally in a total of 7 batches. A total of312 samples were run, using an input of 25 ng (as determined byNanodrop) into the NuGEN FFPE process and hybridized to exon arrays.

Example 2: Fluid Decision (Goldfish)

The aim of this study was to investigate whether the use of RNAprotector TRIzol results in differences in assay performance and if so, whichperforms the best. Samples for this study were 10 paired ex vivo FNAsamples collected in both TRIzol and RNAprotect. Additionally, samplescomposed of mixtures of NHP:PTC were set up in triplicate at a ratio of100:0, 80:20, 60:40 and 0:100, respectively.

In summary, 3 out of 4 prehyb+post-hyb metrics showed significantdifferences between RNAprotect and TRIzol. Additionally, when analyzingall samples by PCA, samples cluster by media type, not biology in thefirst two PCs. Analysis of heatmaps generated from 2700 DE genesdiscovered from tissue and 230 DE genes indicate that biology waspreserved when clustering samples. Within the mixture analysis study,RNAprotect seems to perform better than TRIzol; also RNAprotect is lessvariable than TRIzol. This study supports the continued use ofRNAprotect as the thyroid FNA collection fluid.

Example 3: NuGEN FFPE vs Pico Amplification Protocol Decision

A total of 22 samples were included in this study, made up of bankedFNAs, ex vivo FNAs and prospective FNAs all collected in TRIzol. Thesesamples were chosen to cover a range of RIN values. RNA was sent to alaboratory services vendor and processed through the NuGEN WT-OvationFFPE and Pico amplification kits. Intended input concentrations for thisassay were 100 ng for FFPE and 50 ng for Pico. However, subsequentre-quantitation of a subset of these samples with Nanodrop indicatesthat lower concentrations were used. This observation equally impactedFFPE and Pico results.

The results indicate that the NuGEN FFPE kit performed better than Picoin these samples. Importantly, both methods were useable for samples ofRIN>2. Neither assay was able to rescue highly degraded samples with aRIN<2.

Example 4: Platform Evaluation for Use of Degraded RNA Samples

This study was initiated to determine which technology was best suitedto dealing with a range of good quality and degraded RNA samples. Theplatforms that were evaluated were the Affymetrix Exon Array with theNuGEN WT-Ovation FFPE process carried out at a laboratory servicesvendor (50 ng input), the Illumina whole genome DASL assay carried outat Expression Analysis (using 100 ng input RNA), and the NanostringnCounter system carried out at Nanostring (100 ng and 200 ng input RNA).RNA samples were a selection of banked FNAs, ex vivo FNAs andprospective FNAs all collected in TRIzol. The samples had a range of RINvalues (1-8) from highly degraded to intact samples and two samplesubtypes (NHP/PTC).

In summary, although Nanostring was the only platform that did notrequire amplification of RNA, it was slightly less robust than Exon/FFPEand DASL at low RIN ranges, possibly due to the placement of primers atapproximately 200 bp. The Exon/FFPE and DASL systems are comparableacross the range of sample characteristics tested. While the IlluminaWhole-Genome DASL array performed as well as the NuGEN FFPE/Affymetrixexon assay the decision was made to proceed with the NuGEN/Affymetrixsystem because of FDA readiness considerations of the various microarrayplatforms.

Example 5: Titration of Input Amount (Nemo)

This study was designed to investigate lower input of RNA into the NuGENWT-Ovation FFPE amplification system followed by hybridization onAffymetrix exon arrays. Samples for this study were seven ex vivo FNAsamples collected RNAprotect. This study was split into two arms, A andB, with each arm run by a different operator. Each ex vivo FNA samplewas run internally using an input of 25 ng, 15 ng and 10 ng total RNA(as determined by Nanodrop) into the NuGEN FFPE process and hybridizedto exon arrays. Matching samples from the fluid decision (Goldfish)study run using an input of 50 ng total RNA were included in thisanalysis.

It was noted that Arm B samples showed poorer pre-hyb metrics at theSense strand stage (ST-yield) and that the SPIA cDNA yield (WT-yield)was higher in this experiment than that observed in the Goldfish fluiddecision study but no severe batch effect was observed by PCA analysisof expressed genes. From analysis of post-hyb metrics and gene levelexpression, the input amount of 15 ng and 25 ng seem comparable, howeverexperiment may lack power to detect changes between them, whereas 10 ngshow clear differences from the rest. The conclusion from this study wasto use 25 ng of total RNA as input amount into the NuGEN WT-Ovation FFPEamplification system starting with the FNA in TRIzol internal discoveryset.

Example 6: Titration of Input Volume

The purpose of this experiment is to test for the effect of increasedtotal input RNA volume or Speed Vac concentrated input RNA on the SPIAcDNA and ST cDNA yield and the gene expression data from the NuGEN FFPEExon Array Protocol. The results from this experiment show that a SpeedVac concentration of RNA to reduce starting volume has no measurableeffects on RNA by Bioanalyzer results in terms of size distribution andRIN values. Cross-contamination of samples was not detected in the waternegative control by Bioanalyzer graphs from before and after Speed Vacconcentration, ruling out the Speed Vac as a source of carry-overcontamination. The increased input RNA volume up to 2× standard volumedoes not affect 2×SPIA cDNA and ST cDNA amplification or quality basedon OD260/280, OD260/230, total yield and Bioanalyzer graphs and RIN andthat overall, gene expression was not affected by the increased inputRNA volume based on post hybridization quality or gene expression. Inconclusion, this study demonstrated that either using Speed Vac toconcentrate starting RNA volume or increasing starting volume up to 2×volume can be used in the Exon SOP without any significant alteration tothe process.

Example 7: Algorithm and Marker Results

The results of Classification applied on various discovery data sets arereported in chronological order of the data sets analyzed. In discovery,first a Tissue data set was analyzed (surgical tissue sample), secondly,a data set composed of a small number of FNAs was analyzed and, lastly,a large set of FNA samples were analyzed in two batches (FNA in TRIzoland FNA in RNAProtect). A .CEL file list which contains the name of theexperiment, sample IDs, and where the samples were processed areincluded in Attachment B (Experimental CEL file).

i. TISSUE Data Set

The phased approach to discovery started with Tissue samples and went onto include a few Banked FNA samples and a large pool of Banked andProspective FNA samples. The first discovery data set was comprised of261 Tissue samples, procured from a clinical tissue vendor and anacademic center and processed at an external laboratory. A Simple PCAplot revealed that the MTC (medullary thyroid cancer) and LcT(Lymphocytic Thyroiditis) were easily separable, but the rest of thebenign and malignant subtypes were indistinguishable from this view. Asophisticated, non-linear algorithm working in multi-dimensional spacewas in order. Clustering, an unsupervised technique, showed preliminaryevidence of structure of disease subtypes in Tissue samples.

Two hundred and sixty one (261) .CEL files arrived at Veracyte from thelaboratory services vendor and some samples were excluded because theywere not benign or malignant categories of primary interest (n=28), orthey were normal thyroid (n=12) or they were ATC/MTC (n=25). Somesamples were removed for QC reasons (n=5) (Intron/Exon separationAUC<0.73). This resulted in 179 .CEL files for subsequent analysis.

Classification results using a variety of classifiers yielded about a15% error rate (overall) with just over 100 genes. The error ratesvaried by subtype. Higher error rates were observed for benign sampleswith malignant counterparts (FA vs FC, HA vs FC). The results show thatpair-wise comparison of subtypes yield a very different picture ofaccuracy and number of differentially expressed (D.E.) genes: the harderthe separation between a pair of subtypes, the fewer the number of D.E.genes and higher the error rate.

MTC separation was a relatively easier task in the mix of subtypes,while many differentially expressed genes were detected, only 3literature markers were required to clearly distinguish the class fromothers.

Initial planning was done using the fold-change values observed in theTissue data set to estimate the number of FNA samples that were going tobe needed for discovery. A fold change of 1.8 was observed for a typicalD.E. gene in the PTC category and a minimum of 60 samples were necessaryto train for the classifier for this subclass. The discovery data setsgenerated subsequently, comprised of FNAs, were well above this samplesize as prescribed by this planning analysis.

An initial marker list was also generated from a subset of the tissuesamples at this time and the markers were filed for patent application.The Tissue samples (n=75) were used to extract B vs M markers (N=2765)at gene level, D.E. markers using probeset level analysis (1740) andalternatively spliced genes (N=2868). The union of these marker sets wasa list of 4918 unique markers.

TISSUE+Banked FNA Data Set

The Tissue data set was augmented by 45 banked FNA samples of wellrepresented subtype mix from one investigator. The RNA was extractedusing TRIzol. They were processed by a laboratory services vendor.However the samples were marked by poor RNA quality. The FNA samples hadmore variable Intron-exon separation AUC than the Tissue samples. Usingthe AUC=0.75 threshold 20 .CEL files were removed. One ATC .CEL file wasalso removed resulting in a total of 24 FNA samples to be analyzed.

The two primary objectives were to assess the degradation impact inthese FNA samples and to investigate whether the biological signal foundin Tissue samples transfer to the banked FNA samples despitedegradation. Known, high intensity D.E. markers, e.g., MET, were foundto be class separating in Surgical Tissue samples very well, butmoderately on Prospective FNA samples and very poorly on banked FNAsamples. Classification performance dropped in the Banked FNA samples.For gene-level classification, the error rate jumped from 16.3% (VIKING)to 32% (Banked FNA). Two main sources of differences were identified forthe performance gap from Tissue samples: (1) Technical: sampledegradation and small cellular yields/sample quality and (2) Biological:class heterogeneity, cellular mixture.

Fold-change (PTC vs NHP) in TISSUE samples vs FNA Samples (Banked)yielded a slope of 0.6, intercept=0.08 and R²=0.78, Fold-change (PTC vsNHP) in TISSUE samples vs FNA Samples (Banked) yielded a slope of 0.85,intercept=0.24 and R²=0.79. The concordance between the fold changelevels in the prospective FNA set compared to the Tissue set was thepreliminary evidence we needed to proceed to the next level of discoverywhere we would accrue, process and analyze a large pool of FNA samples,a substantial proportion of them being prospective FNAs.

The results of applying the Combo Marker list process to surgical tissuesample+the Banked FNA data set is a total of 230 markers. Theperformance of the Combo marker list (n=230) looked promising whenapplied to a limited set of 18 prospective FNA samples. However the truetest of this marker list was the classification accuracy of subsequentdata sets (FNA in TRIzol and FNA in RNAProtect).

FNA in TRIzol

The first complete data set of Thyroid FNA samples consisting ofcytology codes=I, B, M or U were available to us to discover marker setsand build our classifier in March 2009. FNA in TRIzol was a welldesigned experiment consisting of 206 FNA samples. 22 samples in thisdata set were from the FFPE experiment, 36 samples were from Ex-vivo FNAusing TRIzol or RNAProtect experiment. FNA in TRIzol also contained 48samples run by an external laboratory services vendor. In addition, FNAin TRIzol had 95 unique samples that were run at Veracyte. Five samplesdid not generate .CEL files, 14 samples failed to meet the AUC criterionof 0.73, 17 .CEL files did not meet the pDet criterion of 0.21. Furtherduplicated samples (which were run at an external laboratory and anin-house or internal laboratory) were removed. Samples with CYN (ColloidNodule), MTC and ATYP subtypes were removed leading to 127 .CEL files tobe analyzed.

The classification error rate as shown in the gene titration curve waspoor at 18%. The Combo marker list (K=230) reduced the rate to 13% butstill was considered to be too high. It was found that the Banked FNAsamples (n=67) from a particular investigator (called cFNA samples)looked very different from the rest of the samples with respect to (i)WTA conc and (ii) pDet. These samples did not vary that much in ST concor AUC. These samples were dropped from subsequent analysis. Theclassification error rates in the remaining samples (n=86) dropped to˜10%. A simple classifier, which took the signs (+1 for up-regulationand −1 for down-regulation) of the markers and added them up, performedwell in separating the benign from the malignant classes.

FNA in TRIzol+FNA in RNAProtect

The second discovery data set was named FNA in RNAProtect. A carefullyplanned experiment yielded in 312 FNA samples to be processed atVeracyte in six batches (G1-G6). Of these 9 samples did not generate CELfiles. Heavy primer amplicon contamination was observed in the lab afterG1-G3 were run (see description of contamination detection and clean upin section 10.1.6 mNuGEN Amplification). As a result, thirteen sampleswere excluded from these batches due to heavy contamination. The usualtwo post-hyb metrics, AUC and pDet (pDABG) were used to filter out moresamples. The Intron-exon separation AUC <0.73 criteria excluded only 2samples. The pDET criteria <0.21 did not apply to the FNA in RNAProtectexperiment. Many samples in this experiment, unlike FNA in TRIzol, fellbelow pDET=0.29.

First Data Set

A higher threshold of 0.29 was used in the data set which will beexplained later in this section. Using this higher threshold, 128samples dropped out from the analysis flow. This is roughly 40% of thesamples. Lastly nodule size exclusion criterion of 1 cm and those withmPTC and mFVPTC surgical pathology labels were dropped from the analysis(6 samples). At the end we analyzed data from 227 samples.

The subtype representation in this data set is: CN:8, CYN:3, FA:19,FC:4, FVPTC:20, HA:7, LCT:30, NHP:86 and PTC:50. Classification errorrate was approximately 10%. MTC samples (n=4) could be differentiatedperfectly from all other subtypes with very few markers). The # genes byerror titration curves were generated using both LIMMA (univariate geneselection) and Random Forest (multivariate gene selection). Thetitration curves for the 5 classifiers look pretty stable at this errorrate after K=60 genes onwards. The gene by error rate titration curvewas regenerated using the re-sampled subtype prevalence in indeterminatecategory. The error rate increased to about 16%. When the error rateswere examined in the cytology=Indeterminate category only withre-sampled weights from subtype prevalence in the indeterminatecategory, the error rate jumped to 25%. The error titration curves weregenerated using both LIMMA (univariate gene selection) and Random Forest(multivariate gene selection).

In addition to cross-validate mode, the split-sample mode was used totrain the classifier on cytology code=B, M or U and predict on cytologycode=I samples, The error rate was 23% which was close to the lastcomparison stated in the previous paragraph. This is the best estimateof overall error rate in samples in cytology code=I category. Technicalfactor removal algorithm was applied to make the samples in TRIzol andRNAprotect more comparable. The resulting improvement is subtle at theclassification error rate level but at the intensity levels theslope=0.88 improves to slope=˜1 amongst the markers.

Second Data Set

Starting with n=227 data set, we restricted the data set to RNAprotectsamples to assess whether classification rate remains the same or not.For our clinical trials, the molecular classifier will be calling benignor not benign on FNA samples collected in RNAprotect only. Data from 135samples in this data set are analyzed.

The subtype representation in this data set is: CN:7, CYN:3, FA:10,FC:3, FVPTC:9, HA:1, LCT:18, NHP:55 and PTC:29. Classification errorrate was approximately 5%. The # genes by error titration curves weregenerated using both LIMMA (univariate gene selection) and Random Forest(multivariate gene selection). The titration curves for the 5classifiers look pretty stable at this error rate after K=40 genesonwards. The gene by error rate titration curve was regenerated usingthe re-sampled subtype prevalence in indeterminate category. The errorrate increased to about 10%. When the error rates were examined in thecytology=Indeterminate category only with re-sampled weights fromsubtype prevalence in the indeterminate category, the error rate jumpedto 22%. The error titration curves were generated using both LIMMA(univariate gene selection) and Random Forest (multivariate geneselection).

The ROC curves for this data set estimated a sensitivity of 82% andspecificity of 95.5%. The black ROC curves are the estimates ofsensitivity and specificity for the subtype prevalence present in thedata set. The red ROC curves are re-drawn with the estimatesrecalculated when the subtype prevalence is what should from thecytology=I category.

The results of applying the COMBO Marker list process to VIKING+FNA inTRIzol_FNA in RNAProtect data sets is a total of total 226 markers. Theintersection of the COMBO process applied to various data sets givehighly overlapping marker sets.

pDET Threshold of 0.29 Derived

The pDET QC metric with a threshold of 0.29 was used in this data set toenable discovery on high quality samples. The lab contamination whichaffected sample quality differentially over different batches led us toexamine G1-G3 separately from batches G5-G7. Lab was cleaned afterfinishing G3. Sample performance in the classifier looked worse afterthis lab cleanup. Since batch G5 was heavily biased towards high qualitysamples, it did not show poor performance as G4, G6 and G7. Theclassifier was built on the good batches (G1-G3, G5) discarding thesamples which were affected by contamination and predicted on samples inG4, G6, G7. The results showed that several PTC samples have amalignancy score of 0 just like the benign samples in this test set. Themisclassification of the PTC samples was heavily correlated with twopost-hyb QC metrics: pDET and AG/Core overlap. The threshold of 0.29 wasthus derived as a necessary condition for a sample to classifycorrectly.

pDET was modeled in a linear (ANOVA) model with several upstream QCmetrics. After controlling for many factor, it appeared that the batchvariable (later FNA in RNAProtect batches) were worse than earlier FNAin RNAProtect batches. Similarly low Bioanalyzer input amounts, lowST.conc and low WTA.conc were also indicative of poor pDET valuesdownstream. These results were statistically significant.

FVPTC, cytology=I vs FVPTC, cytology=M comparison

Error rates were investigated by subtypes in cytology=I class vscytology=B, M class. The malignant category where the classifierperforms the best is PTC. In the FVPTC class was different: the errorrates were only 9% in FVPTC, cytology=M subclass, but was 77% in theFVPTC, cytology=I subclass. This distinction between error rates wasstatistically significant. Investigating this further showed that FVPTC,cytology=I were indistinguishable from NHPs. The UP and down regulatedmarkers have become insensitive in this class.

Habitual Offenders

The frequency of misclassification for the classifier was plotted to seewhich samples the algorithm is prone to misclassifying. A score of 1 inthe y-axis designates that the sample is misclassified by each type ofclassifier algorithm embedded in Classification. As predicted, manyFVPTCs are on the list, however some are exonerated after expertre-reads.

Bloodiness

The lab scored about 159 samples on a 4-point scale of bloodiness: b0,b1, b2 and b3, where the b0 is the least bloody and b3 is the mostbloody sample category. These were more accurate on the RNAprotectsamples, since TRIzol also lyses blood cells and therefore it is hard tomake a visual call of bloodiness by lab personnel. A gene-expressionbased marker was derived for bloodiness based on the genes shown inTable 4.

TABLE 4 List of Markers for Bloodiness Probe set ID Gene Symbol GeneDescription 3642664 HBA1 blood, hemoglobin 3642675 HBA1 blood,hemoglobin 3642664 HBA2 blood, hemoglobin 3642675 HBA2 blood, hemoglobin3360401 HBB blood, hemoglobin 3360417 HBB blood, hemoglobin 3360432HBBP1 blood, hemoglobin 3360417 HBD blood, hemoglobin 3360456 HBE1blood, hemoglobin 3360441 HBG1 blood, hemoglobin 3360456 HBG1 blood,hemoglobin 3360441 HBG2 blood, hemoglobin 3360456 HBG2 blood, hemoglobin3642654 HBM blood, hemoglobin 3642687 HBQ1 blood, hemoglobin 3642643 HBZblood, hemoglobin 3642652 HBZ blood, hemoglobin

The gene expression marker was available on all samples and correlatedwell with the laboratory visual marker when it was available. Thecontinuous score of this marker was divided into quartiles: m0, m1, m2and m3. Classification error rates were investigated in the 4 categoriesof both the laboratory-based and gene-expression-based bloodinessmarker. Classification rates did not seem to vary over these categories.Marker lists were derived with no adjustment for bloodiness (by means oftechnical factor removal), by adjustment for bloodiness with presence ofdisease label in model and by adjustment for bloodiness without presenceof disease label in model. The marker lists were compared and found tobe completely overlapping for 85% of the genes. This confirmed thatbloodiness did not impact classification accuracy or marker list in thisdata set of RNAprotect samples.

While preferred embodiments of the present invention have been shown anddescribed herein, it will be obvious to those skilled in the art thatsuch embodiments are provided by way of example only. Numerousvariations, changes, and substitutions will now occur to those skilledin the art without departing from the invention. It should be understoodthat various alternatives to the embodiments of the invention describedherein may be employed in practicing the invention. It is intended thatthe following claims define the scope of the invention and that methodsand structures within the scope of these claims and their equivalents becovered thereby.

REFERENCES

-   Baloch, Z. W. and V. A. LiVolsi (2007). “Our approach to    follicular-patterned lesions of the thyroid.” J Clin Pathol 60(3):    244-50.-   Diaz-Uriarte, R. and S. Alvarez de Andres (2006). “Gene selection    and classification of microarray data using random forest.” BMC    Bioinformatics 7: 3.-   Fishel, I., A. Kaufman, et al. (2007). “Meta-analysis of gene    expression data: a predictor-based approach.” Bioinformatics 23(13):    1599-606.-   Gombos, K., E. Zele, et al. (2007). “Characterization of microarray    gene expression profiles of early stage thyroid tumours.” Cancer    Genomics Proteomics 4(6): 403-9.-   Ito, Y., T. Yabuta, et al. (2008). “Distant and lymph node    metastases of thyroid nodules with no pathological evidence of    malignancy: a limitation of pathological examination.” Endocr J    55(5): 889-94.-   Kashigina, E. A., S. G. Girshin, et al. (1980). “[Metastasizing    adenoma of the thyroid].” Vestn Rentgenol Radiol(3): 68-70.-   Mizukami, Y., A. Nonomura, et al. (1996). “Late bone metastasis from    an encapsulated follicular carcinoma of the thyroid without capsular    and vascular invasion.” Pathol Int 46(6): 457-61.-   Smyth, G. K. (2004). “Linear models and empirical bayes methods for    assessing differential expression in microarray experiments.” Stat    Appl Genet Mol Biol 3: Article3.-   Tadashi, T. (2009). “Brain metastasis from thyroid adenomatous    nodules or an encapsulated thyroid follicular tumor without capsular    and vascular invasion: a case report.” Cases Journal 2(7180).-   Tzen, C. Y., Y. W. Huang, et al. (2003). “Is atypical follicular    adenoma of the thyroid a preinvasive malignancy?” Hum Pathol 34(7):    666-9.

What is claimed is:
 1. A method for outputting a report that identifiesa classification of a sample from a subject as benign or non-benign forlung cancer, comprising: (a) providing said sample from said subject,which subject has or is suspected of having said lung cancer; (b)subjecting a first portion of said sample to cytological testing thatidentifies said sample as ambiguous; (c) after identifying said sampleas ambiguous, assaying by sequencing, array hybridization, or nucleicacid amplification one or more gene expression products in a secondportion of said sample, to generate a data set including datacorresponding to a level of said one or more gene expression products;(d) inputting said data into a trained algorithm to generate aclassification of said sample as benign or non-benign for said lungcancer, wherein said trained algorithm has been trained with a trainingset of samples comprising known benign samples and known non-benignsamples; and (e) electronically outputting said report that identifiessaid classification of said sample as benign or non-benign for said lungcancer.
 2. The method of claim 1, wherein said sample comprisesepithelial cells.
 3. The method of claim 1, wherein said data set isgenerated by selectively removing a plurality of technical factors. 4.The method of claim 1, wherein said sample is independent of saidtraining set of samples.
 5. The method of claim 1, wherein said lungcancer comprises a lung carcinoid tumor.
 6. The method of claim 1,wherein said one or more gene expression products comprises messengerribonucleic acid (mRNA).
 7. The method of claim 1, further comprisingmonitoring a change over time in said classification by repeating(a)-(e) in another sample from said subject.
 8. The method of claim 1,wherein said report is presented on a computer screen.
 9. The method ofclaim 1, wherein said first portion of said sample is different thansaid second portion of said sample.
 10. The method of claim 1, wherein(a) comprises obtaining a swab from said subject.
 11. The method ofclaim 10, wherein said swab is a buccal swab.
 12. The method of claim 1,wherein (a) comprises obtaining swabs from said subject, wherein saidfirst portion of said sample is derived from a first swab and saidsecond portion of said sample is derived from a second swab.
 13. Themethod of claim 1, wherein said data does not include a plurality oftechnical factors.
 14. The method of claim 1, wherein said non-benignsample is cancerous or suspicious.
 15. The method of claim 1, whereinsaid non-benign sample is malignant.
 16. The method of claim 1, furthercomprising training said trained algorithm prior to (a).
 17. The methodof claim 1, wherein said report identifies said classification of saidsample as non-benign for said lung cancer and comprises a likelihood ofcancer or malignancy.